[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] Confined to a particular stage of social development, most existing communities in China have been designed with young and middle-aged people as the targets, neglecting the housing needs of the elderly. In addition, there is a considerable lack of elderly facilities and support services in community living areas. Therefore, expediting the development of age-friendly communities is a crucial initiative to improve the living environment of elderly residents and address the effects of aging. The development of age-friendly communities is an urgent topic that demands immediate attention. Unfortunately, there is still no systematic evaluation study on the effect of establishing age-friendly communities. [Methods] Using data from a survey of residents of Beijing's first batch of national model age-friendly communities, we examined the effect of age-friendly community establishment on the life satisfaction levels of the elderly as well as the mediating role of their mental health. We also tested the robustness of the results based on a propensity score matching model. In addition, subjective and objective indicators of the seven functions of an age-friendly community were used to test the effectiveness of the age-friendly community functions. Finally, we employed grouped regression to test the heterogeneity of age-friendly community well-being among different elderly groups. [Results] The results demonstrate the following: (1) The establishment of age-friendly communities enhances the life satisfaction and mental health of the elderly, and their mental health mediates the relationship between the establishment of age-friendly communities and their life satisfaction. (2) The seven functions of age-friendly communities affect the life satisfaction of the elderly to varying degrees, and only four functions—community atmosphere, community service, smart elderly care, and social participation—improve the mental health of the elderly. (3) The establishment of age-friendly communities more strongly affects the life satisfaction and mental health of older people who are younger, more capable of self-care, more educated, and lower income group. [Conclusions] The abovementioned results show that overall, China's current age-friendly communities have achieved some success; however, some aspects still require attention during the follow-up promotion of age-friendly communities. First, when promoting the establishment of age-friendly communities, we should not only focus on improving the physical environment of the community but also consider various aspects of the social environment to help alleviate the mental health problems of the elderly. Concurrently, we should give more attention to the needs of vulnerable groups, such as the elderly with low education levels, limited self-care ability, and low incomes. We should implement policies such as home care, group assistance, and simplified and inexpensive procedures so that the benefits of age-friendly community establishments can be enjoyed by as many people as possible.

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

[Objective] Progress management is an important part of construction management, which helps effectively reduce the risk of project delay. Its main objective is to monitor the actual construction progress and compare it with the construction plan. The traditional method of progress updating relies on manual checking and recording, which not only lags behind but also is prone to recording errors. Following the development of building information modeling (BIM), technologies such as the internet of things (IoT), point clouds, and visual images have been gradually applied to construction progress identification and plan comparison. However, these methods require the introduction of additional acquisition equipment, and point cloud acquisition equipment is costly. In addition, image processing is easily affected by factors such as occlusion, light, and weather. Therefore, the present study proposes a construction progress updating method based on BIM and large language models (LLMs). This approach enables construction personnel to verbally report progress information to the LLM, allowing a three-dimensional (3-D) building model to be accordingly updated. [Methods] This research develops a system that automatically extracts relevant information from natural language, which identifies the corresponding component using the planned construction time in the component database and visualizes the progress status of the 3-D building model in Blender. The system does not require detailed information such as precise component IDs but completes the progress update by recognizing fuzzy information (e.g., construction section, floor, and other relevant information). Specifically, this study first parses the industry foundation classes (IFC) format BIM file and construction schedule to extract and correlate the component IDs, location information, and scheduled construction time. It then constructs a database of building components. Subsequently, the LLM is enhanced through prompt engineering so that it can generate accurate information query instructions based on natural language inputs, retrieve component information from the database, assess the progress status, and generate corresponding model update instructions to achieve dynamic updates in Blender. [Results] This study tested the accuracy and consistency of the proposed method using a BIM model with 716 components and a dataset of 200 progress reports in various natural language formats. The testing results showed that after prompt fine-tuning, the LLM-based method achieved an average accuracy of 96% in progress assessment and model updating and 62.5% improvement over the non-fine-tuned model. The consistency reached 87% or an increase of 68% over the non-fine-tuned model, demonstrating the effectiveness and feasibility of this method for construction progress updating. [Conclusions] This study has successfully combined BIM and LLMs to develop a construction progress updating method, including the construction of component retrieval database and schedule updating process based on LLMs. The case studies show that the method effectively improves the accuracy and consistency of the LLM in generating progress update instructions without providing additional equipment and significant computing costs. The method allows construction personnel to describe progress information in natural language and achieves accurate progress updating of the 3-D model of a building, which meets the demand for visualizing and updating progress information on construction sites. However, this study suffers from certain limitations. Due to the use of prompt fine-tuning for the LLM, consistency remains a challenge. Future work is expected to improve the model's accuracy and consistency by training a local model.

[Objective] Crane lifting is a critical process in steel and concrete structure construction, but safety accidents occur frequently, necessitating effective preventive measures. Traditional supervision relies on human experience and on-site judgment, which are vulnerable to operator fatigue and distraction. As information technology advances, it plays a vital role in crane safety risk monitoring. However, current research mainly focuses on monitoring lifted objects, with insufficient comprehensive studies on the cranes' overall workspaces. In terms of worker monitoring, existing wireless sensing methods perform poorly in steel structure construction scenarios due to significant interference, while visual methods mostly focus on tracking workers' locations in the image, lacking their accurate real-world coordinates. This study aims to propose a spatial collision monitoring method that integrates building information modeling, crane sensing, and computer vision technologies to enhance safety monitoring of crane-worker interactions in steel structure construction scenarios. [Methods] The specific research framework and methods are illustrated as follows: First, a crane's workspace is categorized into low-risk, medium-risk, and high-risk areas, and equations for defining medium- and high-risk boundaries are established. Then, the crane's location and posture are monitored in real time using sensors. Simultaneously, using the YOLO11-OBB model and perspective transformation, the actual location of the workers on the floor is calculated based on precalibrated reference points and their location in the image. Finally, the on-site monitoring data are integrated into a 3-D management platform, which calculates and visualizes the spatial collision risks between the crane and the workers in real time. [Results] A case study from a steel structure construction project in Xi'an was used to test the accuracy and feasibility of the proposed method. The test results showed that the monitoring error for the medium-risk and high-risk crane workspaces was ±2.600 and ±2.611 m, respectively. The accuracy of the YOLO11-OBB model for worker localization and recognition was 0.968, with a recall rate of 0.969, the mean average precision at intersection over union (IoU) 50% (mPA50) of 0.980, and the mean average precision at IoU 50%-95% (mPA50-95) of 0.864. The mean absolute error, mean relative error, and root mean square error for the calculation of the actual locations of the workers were 67.44 mm, 4.32%, and 86.16 mm, respectively. During the 9-month monitoring of the site, the frequency of workers entering high-risk areas showed a fluctuating decline, demonstrating the feasibility of the method in enhancing safety warnings on construction sites. [Conclusions] This study presents a spatial collision monitoring method for crane and worker interaction in steel structure construction scenarios, including the definition and monitoring of crane hazardous workspaces and worker locations. A 3-D visualization platform is used to monitor the collision situation between cranes and workers. The case study indicates that using only regular cameras and without the need for workers to wear additional monitoring devices, the method can effectively ensure the accuracy of worker localization within a distance of 20 m from cameras. The method also enables differentiated responses to cranes operation in spaces with varying risk levels, allowing managers to intuitively view the spatial collision status of the crane and workers through the 3-D model on-site.

[Objective] To achieve energy-saving and emission reduction in buildings, green building design is increasingly gaining attention. However, traditional design methods often rely heavily on the designer's experience, which complicates the consideration of multidimensional factors such as technical strategies and costs, thus limiting decision-making efficiency. Mining tacit knowledge to support green building design decisions and improve decision-making efficiency presents a significant challenge. [Methods] This study proposes a two-stage intelligent decision-making model for energy-saving building strategies based on tacit knowledge. The first stage employs a case-based reasoning (CBR) model to determine energy-saving technical strategies. A case library containing 147 green-certified buildings provides reference strategies using attributes from the preliminary design phase, such as building type, structure, number of floors, height, orientation, shape coefficient, floor area, and green certification level. Cosine similarity helps retrieve relevant cases and identify technical strategies like window-to-wall ratios, heat transfer coefficients of the building envelope, heat pump loads, and renewable energy use. The second stage involves an incremental cost prediction model that uses machine learning algorithms. A 2∶8 split of the case library into test and training sets enables comparison across four machine learning algorithms: artificial neural network, extreme gradient boosting (XGBoost), support vector machine, and random forest. Each model's prediction accuracy, precision, and F₁ score (the harmonic mean of precision and recall) are evaluated. The model takes the technical strategies identified in the first stage and the known information from the preliminary design phase as input feature parameters. The import_plot module analyzes feature importance to eliminate redundant features. The two-stage model is validated on buildings from regions with hot summers and cold winters. [Results] Findings indicate the following: (1) The CBR model effectively identifies and reuses the most similar energy-saving technical strategies, thereby improving decision-making efficiency. Most target cases achieve a similarity greater than 0.8 in the case library. (2) Among the machine learning models, the XGBoost-based incremental cost prediction model exhibits the highest accuracy, achieving 72.41%. (3) By applying the synthetic minority oversampling technique to balance samples and remove outliers, the prediction accuracies for four types of costs reach approximately 70%. However, the prediction accuracy for the fifth type of incremental cost is lower owing to varying owner preferences and requirements. [Conclusions] The proposed two-stage intelligent decision-making model successfully integrates the CBR model with machine learning algorithms. The proposed model optimizes the use of limited known information available during the preliminary design stage to predict both technical strategies and incremental costs. This model enhances the scientific rigor and efficiency of energy-saving decision-making, providing significant support for green building design.

[Objective] Efficient transfer of tacit knowledge is crucial for enhancing resilience and promoting collaborative innovation among construction project teams. Although most studies focus on knowledge transfer between organizations or between organizations and individuals, they often rely on subjective reports to explore influencing factors and mechanisms. Given the inherent nature of tacit knowledge, its transfer process involves complex cognitive interactions and integration among parties, which traditional monitoring methods fail to fully capture. Therefore, this study aims to reveal the micro-level mechanisms of tacit knowledge transfer between individuals in construction projects from a cognitive perspective. [Methods] This study categorizes tacit knowledge of construction projects into cognitive and technical types. The study involved 96 healthy university students divided into 48 pairs. Each pair included one sender with engineering knowledge and one receiver without. These pairs were randomly assigned to either a cognitive or technical group, each containing 24 pairs, to perform tacit knowledge transfer tasks relevant to construction projects. Brain oxygenation data were monitored using near-infrared equipment during the transfer process. A general linear model was used to calculate β-values for each channel in both groups based on the modified Beer-Lambert law, representing brain region activation. Hyperscanning technology extracted the preprocessed oxyhemoglobin time series for both the sender and receiver. Wavelet transform coherence was employed to model interpersonal brain synchronization (IBS) activities. The study then examined the relationship between IBS during the transfer and the performance of knowledge transfer measured by the efficiency of knowledge utilization and the degree of knowledge internalization. [Results] The results indicated the following: (1) Both senders and receivers showed significant brain activation during tacit knowledge transfer. Receivers in the cognitive group exhibited higher brain activation than those in the technical group, indicating that transferring cognitive tacit knowledge demands greater neural engagement. (2) Significant IBS was observed between senders and receivers during the transfer tasks. The cognitive group exhibited lower IBS levels compared to the technical group, implying that the complexity of cognitive tacit knowledge might reduce immediate neural synchrony despite increased brain activation. (3) A strong causal relationship was found between IBS levels and the dual-dimensional performance of tacit knowledge transfer in terms of both knowledge utilization and internalization by the receiver. Higher IBS levels were associated with better transfer performance, highlighting the importance of neural synchrony in successful tacit knowledge transfer. [Conclusions] This study sheds light on the cognitive processes involved in tacit knowledge transfer within construction project teams, highlighting differences in cognitive processes across different types of tacit knowledge. These findings underscore the critical role of IBS in predicting and enhancing knowledge utilization and internalization by the receiver, providing a reliable indicator for successful tacit knowledge transfer. These insights contribute to a deeper understanding of how cognitive interactions enhance project team efficiency, paving the way for improved strategies to enhance teamwork and innovation in construction projects.

[Objective] The condition of utility corridors, a critical component of urban infrastructure, is crucial for the public safety. However, underground utility corridors often have long routes and traverse complex geological areas, making structural inspections extremely difficult. Disturbances such as ground deformation, loads from the overlying strata and surrounding buildings, and nearby construction activities can cause uneven settlement, leading to severe cracking and leakage. Existing settlement sensing devices, such as stress-strain sensors, fiber-optic sensors, and inspection cameras, are often expensive and complex to install. This study proposes a more efficient and simplified vision-based method for radial monitoring of utility corridor sections using multiple feature planes. [Methods] This study employed a template matching method to track target movements across multiple planes. By tracking predefined targets and detecting circles within the region of interest using the Hough circle transform, spatial changes were recorded. The template matching algorithm determined the spatial position of detection targets in consecutive frames for each monitoring section. The matching algorithm generated a similarity index for the detection target, and by integrating all detection results, a similarity matrix could be obtained. This matrix helped detect target positions across frames by mapping indices of the extrema and scaling factors to the original frames. The proposed method then adjusted and integrated these scaling factors to achieve real-time settlement detection of multiple radial sections. The underground utility corridor beneath the Ciyunsi Bridge in Beijing was used as a case study to validate this method. [Results] The experimental results yield the following major findings: (1) Detection errors increase as sections move further from the camera but stabilize over time. After five days, errors for sections at 15 m and 30 m converge faster, reaching -5 mm and -8 mm, respectively. (2) Clearance convergence errors can mirror settlement trends, with smaller errors near the camera and larger for sections further away. Yet, all errors converge to specific boundary values. Sections at 45 m and 60 m, which have larger errors, converge to -12 mm and -16 mm, respectively. (3) Environmental factors have minimal impact on errors, particularly in sections close to the camera. Temperature and humidity have a greater impact on the 45-m section, but the correlation coefficient is still low, indicating a limited effect on errors. The correlation between radial convergence errors and environmental factors is similarly low, showing that environmental impacts are minimal. [Conclusions] This study introduces a reliable detection technique leveraging computer vision detection technology by overlaying multiple detection sections and independently adjusting scaling factors. By harnessing radial space features in utility corridors and overlaying independent detection sections, the method enhances the data collection efficiency of the detection equipment. Additionally, overlaying scene pixel points improves data storage efficiency.

[Objective] Large-scale infrastructure is characterized by vast scale, technical complexity, multiple stakeholders, and high risk. Enhancing the efficiency of program management in the context of large-scale infrastructure is crucial for economic and social development in China. Owners are important in the construction of large-scale infrastructure. However, the existing research lacks a comprehensive understanding of key factors involved in program management from owners' perspective and has not systematically explored the underlying mechanisms. This study aims to establish a management capability index system, particularly for program management in the context of large-scale infrastructure from the owners' perspective, and construct and validate a theoretical model for such management activities. [Methods] This study adopted a hybrid approach combining field investigations, surveys, and case studies, focusing on the Shenzhen Bay Super Headquarters Base—a large-scale infrastructure program encompassing 17 projects. During field investigations, qualitative data were gathered based on stakeholders' management needs and challenges associated with large-scale infrastructure program management. Based on an extensive literature review, a questionnaire was designed and distributed to the management and technical personnel operating within the program. This survey generated 407 valid responses, thus providing a robust dataset. The quantitative analysis undertaken employed word frequency and social network analysis to identify primary management demands. Sample mean estimation was employed to gain insights into the current state of program management. Hierarchical cluster analysis was subsequently employed to delineate key owner capabilities critical to effective program management. The proposed theoretical model was tested via partial least squares structural equation modeling (PLS-SEM) to validate the relationships among owners' program-management capabilities, external program conditions, partnership, resource allocation, and program performance. The reliability and validity of the model were rigorously tested, and the model's path coefficients, explanatory power, and predictive power were also assessed. [Results] Textual analysis results identified a pressing requirement for owners to coordinate the design, procurement, and construction stages of projects to optimize the overall effectiveness of program management. Hierarchy cluster analysis results revealed three key program-management capabilities of owners: construction management, dynamic, and information technology capabilities. The PLS-SEM analysis results validated the proposed model, highlighting the notable positive effects of the owners' program-management capabilities and external program conditions on program performance. Specifically, two important paths were identified: (1) owners' program-management capabilities → partnership → (resource allocation) → program performance and (2) external program conditions → partnership→ (resource allocation) → program performance. [Conclusions] The findings of this study offer theoretical insights and practical guidance for enhancing program management in the context of large-scale infrastructure. The findings confirm that the owners are critical stakeholders in managing large-scale infrastructure programs. Comprehensive owners' program-management capabilities can considerably enhance program performance by cultivating partnerships between stakeholders and improving resource allocation. Further, this study highlights the important influence of external program conditions on program outcomes, emphasizing the need for owners to adapt to dynamic external environments. Moreover, practical recommendations are provided for managing large-scale infrastructure programs from owners' perspective, suggesting that owners should systematically identify and address constraints within the program's lifecycle, enhance their dynamic capabilities to effectively adapt to complex external conditions, foster collaborative relationships with diverse stakeholders, and facilitate the integration of advanced information technologies within program management.

[Objective] In the past years, the Chinese government has promoted the implementation of clean energy projects to accelerate the transition to green and low-carbon energy in accordance with its “Dual Carbon” target. Clean energy projects involve complex technical, socioeconomic, and environmental risks that cover all stages of engineering, procurement, construction, and operation. Thus, enhancing risk management is essential for effectively mitigating various risks and improving project performance. Studies on the risk management of clean energy projects often focus on specific perspectives and lack systematic investigations of related management issues of clean energy projects. Given the importance of stakeholder collaboration and the application of digital technology in risk management for clean energy projects, this study constructs and empirically validates a conceptual risk management model from the dual perspectives of digitalization and partnering. [Methods] A mixed-method design was adopted, combining qualitative and quantitative approaches to conduct an empirical investigation. To collect quantitative data, a risk management survey questionnaire for clean energy projects was designed based on a literature review and expert interviews and distributed to management and technical personnel from various clean energy projects. Qualitative data were then gathered through expert interviews, field studies, and the collection of project materials, including digital management materials, engineering contracts, risk management cases, and risk management documents. This study also employed structural equation modeling to verify the relationships between variables in the risk management impact relationship model, including knowledge management, project digitization, partnering, participant capability, project performance, and risk management. The questionnaire survey results were analyzed to assess the current state of various management factors, thus providing insights into clean energy project management and identifying key risk management factors. [Results] The results obtained after validating the conceptual risk management impact relationship model reveal that project digitization can directly influence risk management and can also indirectly enhance risk management by promoting knowledge management and improving participant capability. Furthermore, partnering can improve risk management by strengthening enterprise capabilities and facilitating knowledge management. Finally, risk management has a significant positive effect on project performance. [Conclusions] This study proposes and validates a risk management impact relationship model for clean energy projects and reveals the mechanisms influencing risk management from the perspectives of digitization and partnering. The findings indicate that project digitization, partnering, knowledge management, participant capability, and risk management collectively have a significant impact on project performance. Furthermore, this study establishes a measurement indicator system for factors related to clean energy project risk management, facilitating systematic analysis of the current status, obstacles, and causes of these factors. The outcomes of our industry survey also reveal key aspects that should be prioritized in current risk management efforts. From our findings, several risk management strategies for clean energy projects are proposed, including the establishment of a systematic risk management system to strengthen comprehensive risk management, acceleration of the digital transformation of projects to enhance intelligent risk management, improvement of the partnering among project participants to promote collaborative risk management, implementation of a comprehensive knowledge management system to create a learning organization, and enhancement of participant capability to continuously improve project risk management.

[Objective] The rapid development of clean energy projects under the “Double Carbon” goals highlights the crucial role of environmental management. However, current research often overlooks the impact of cooperation among project participants on environmental outcomes. This study explores how environmental management systems, digital environmental protection, and environmental impact assessments affect the environmental protection management performance of clean energy projects. We focus on building a structural equation model to guide the environmental management of clean energy projects through participant collaboration. [Methods] This study uses a quantitative questionnaire to collect data from owners, designers, constructors, suppliers, and supervisors on key indicators such as participant-builder partnerships, environmental management systems, environmental impact assessments, digital environmental management, environmental management in the construction process, and environmental management of project performance. Using this data, a structural equation model is constructed to measure the path influence coefficients using the quantitative data. [Results] The results show that effective environmental management systems, environmental impact assessments, and digital environmental management promote the construction process and overall project performance. The main role paths identified are the following: “partnership → environmental management system → construction process management → environmental management performance”, “partnership → environmental impact assessment → construction process management → environmental management performance”, and “partnership → digital environmental management → construction process management → environmental management performance”. The research establishes a measurement index system for environmental protection management of clean energy projects. The factors that environmental protection management of clean energy projects should focus on are clarified through the research. Recommendations include: (1) establishing a holistic environmental protection management system to standardize and refine project management; (2) strengthening environmental protection management throughout the project lifecycle, particularly during impact assessment and construction; and (3) implementing digital environmental protection management by establishing an environmental protection information platform to boost efficiency. [Conclusions] This study reveals the influence mechanism of environmental management of clean energy projects, offering empirical support for improvements. It paves the way for realizing efficient environmental protection management for future clean energy projects, improving performance through a robust environmental protection management system, lifecycle management, and digital environmental protection management. The insights provided serve as a reference for environmental protection management of engineering projects including clean energy.

[Objective] Developing clean energy is crucial for achieving China's “double carbon” goals, prompting active investment in clean energy projects across the country. However, these projects face a complex external environment with many stakeholders, which makes large-scale grid integration challenging. Moreover, existing projects often suffer from issues such as insufficient energy allocation regulation, mismatched installed capacity and power demand, low economic efficiency in power transmission, and poor synergy effect among projects, mainly owing to inadequate design management. Although design management is vital in clean energy project management, a lack of research is observed from a collaborative perspective among participants. [Methods] This study adopted a mixed research method that combined quantitative research and qualitative research. Quantitative data were obtained through questionnaire surveys, whereas qualitative data were obtained through semistructured interviews and case studies. Respondents included experienced professionals in clean energy project construction, such as owners, designers, suppliers, constructors, and supervisors. Herein, data analysis involved descriptive statistics, mean ranking analysis, difference testing, cluster analysis, and structural equation modeling. [Results] The results show that partnering, feasibility studies, and owner's design management positively impact project performance through two paths: (1) partnering → feasibility study → design performance → project performance and (2) partnering → owner's design management → (feasibility study) → design performance → project performance. During the feasibility study, designers should deeply analyze the supply and demand of the clean energy power market, identify internal and cross-regional power consumption objects, and completely master the basic information of the design. They should rationally select sites, strengthen technical scheme demonstrations, fully consider site and surrounding area constraints, reasonably plan project resettlement and environmental protection, and ensure the project's economic feasibility. In the design process, owners should fully understand national and regional energy policies, deepen their understanding of the project objectives and design needs, and guide design development effectively. The contract should specify the design scope, depth, quality, and schedule requirements and reasonably define the responsibilities, rights, and obligations of all parties. It is crucial to establish a sound design management system, prepare a clear design schedule, and dynamically adjust the schedule as required. Implementing a sound design review, change, and optimization process, identifying design risks, and establishing corresponding control mechanisms are also essential. Developing operable design assessment indicators, reasonable reward and punishment measures, and a design interface management mechanism for stakeholders, along with actively applying information technology, is recommended. Combined with a case analysis, this study offers design management suggestions. [Conclusions] This study proposes and validates a design management model for clean energy projects, revealing the considerable positive effects and pathways of partnering, feasibility studies, and owner's design management on project performance. Through questionnaires and interviews, this study deeply analyze the current state of the feasibility studies and design management, identifying key issues and factors to focus on in clean energy project design. The results provide theoretical and practical references for managing clean energy project design.

[Objective] Driving under the influence of drugs or drugged driving refers to operating a vehicle after consuming certain drugs, posing a significant risk to public safety. While international research on drugged driving is extensive, domestic studies are lacking. This paper aims to bridge this gap by reviewing international research progress and summarizing specific research directions and achievements to guide domestic research. [Methods] To thoroughly assess the research progress on drugged driving, data was collected from the Web of Science Core Collection Database. The search used keywords such as “drug (medicine) and drive (driving)”, limiting the research direction to “transportation” and publication period from “1999 to 2023”. Totally 264 research articles were gathered. The mapping knowledge domain (MKD) method was used to analyze the annual distribution, source publications, keyword co-occurrence, and other relevant literature aspects, providing specific insights into progress in drugged driving research. [Results] The results show that international research on drugged driving has been extensive and diverse since the 1990s. Qualitative and quantitative studies have explored various aspects of the issue, including the types of drugs affecting drivers, their impact on driving abilities, the risks associated with drugged driving, the prevalence of drugged, driver attitudes and perceptions, drug detection technologies, and relevant legislation. To promote governance and prevent drugged driving incidents in China, several projects need attention: classifying drugs that impair driving and understanding their pharmacological effects, developing drug detection technologies, conducting epidemiological investigations on the prevalence of drugged driving among drivers, and carrying out empirical analysis and legislative research on drugged driving cases. [Conclusions] This paper employs structured network analysis methods to comprehensively review international research achievements in drugged driving during the past 30 years. The analysis of annual publication distribution, source publications, and keyword co-occurrence supplements existing literature reviews. This study offers valuable guidance for future research and governance strategies related to drugged driving in the domestic domain.

[Objective] Decommissioning of nuclear facilities is a critical phase in the lifecycle of nuclear energy utilization, and the safety of the procedure directly impacts environmental and public health. As an increasing number of nuclear facilities worldwide near the end of their designed service life, the question of how to carry out safe and effective decommissioning has become an urgent issue to address. During this process, precisely evaluating the radioactive contamination of structural components is fundamental to formulating decommissioning plans and management measures. Although diverse source term survey methods exist, in practice, because of the inadequacy or inaccuracy of process data, it is frequently necessary to rely on radiation measurement techniques to obtain specific information on radioactive contamination. Therefore, this study aims to develop a measurement technology to enhance the accuracy of measuring the radioactivity distribution of structural components during the decommissioning of nuclear facilities, providing a scientific basis for the secure decommissioning of nuclear facilities. [Methods] This research employs a collimated gamma detector, which primarily consists of a gamma detector and a collimator. The collimator is used to limit the direction of incident rays to enhance the spatial resolution and sensitivity of the measurement. The structural components involved in the decommissioning of nuclear facilities can be categorized as flat plates and pipelines based on their geometric features, each requiring different scanning and measurement strategies. The scanning measurement techniques appropriate for flat panel structural components can be selected based on the contamination type. A circular scanning measurement method is adopted for pipe structural components. During the measurement process, the collimated gamma detector traverses the structural component surface, recording gamma-ray signals from all potentially contaminated areas and conducting preliminary analysis of the collected data to assess data quality and integrity. To determine the radioactivity distribution of the structural components from the measurement data, both equal-resolution reconstruction and super-resolution reconstruction methods are proposed. Equal-resolution reconstruction employs grid sizes that are identical to the collimator's aperture size for gridding the area under test, while super-resolution reconstruction uses grid sizes smaller than the collimator's aperture size to achieve higher resolution. Equal-resolution reconstruction is suitable for tasks requiring faster reconstruction speeds, and super-resolution reconstruction is suitable for tasks demanding higher resolution. Both methods are implemented through iterative algorithms. [Results] The results demonstrate that the methods proposed are effective in measuring the radioactivity distribution of structural components during the decommissioning of nuclear facilities, with good position and angular resolution. By conducting Monte Carlo simulation validation, the relative deviation of the radioactivity derived by both the reconstruction methods is within 10%, and the position resolution at a detection distance of 60 cm derived by equal-resolution reconstruction and super-resolution reconstruction methods reached 3.2 mm and 1.6 mm, with corresponding angular resolutions of 0.3° and 0.2°.The average reconstruction speed of the super-resolution method is slower than that of the equal-resolution method. However, in practical applications, the appropriate reconstruction method that is most suitable for specific needs may be selected. [Conclusions] This study develops a measurement technology for radioactivity distribution based on a collimated gamma detector, providing a novel technical strategy for accurately measuring radioactivity in structural components during the decommissioning of nuclear facilities. The technology enhances the accuracy and resolution of measurements through systematic modeling and algorithm design, providing technical support for the secure decommissioning of nuclear facilities. Future research can further optimize the hardware parameters of the detector, which, when combined with this study's results, provide more comprehensive technical support for the secure decommissioning of nuclear facilities.

[Objective] The wildland-urban interface (WUI) is a transitional area between human habitation and natural ecosystems, such as forests, characterized by complex fuel distributions and diverse combustion properties. With the increasing frequency and scale of WUI fires resulting from urbanization and climate change, the need for accurate fire prediction has become critical. The existing models for predicting fire spread are often inadequate because they either analogize urban buildings to vegetation or do not consider differences in fire spread mechanisms between the two. This study addresses these limitations by proposing an extended multimodel combination method for predicting fire spread in WUIs, particularly by focusing on the interaction between vegetation and urban buildings. [Methods] This study combines vegetation-building and building-vegetation fire models based on the interaction of vegetation and building fires to accurately predict fire spread in WUI areas. A cellular automata approach is used to divide the study area into grid cells, allowing for the modeling of fire spread across different cell types with unique state and combustion rules. Two specific models are built: a vegetation-building fire model, which evaluates the likelihood of buildings being ignited by adjacent burning vegetation on the basis of thermal radiation, and a building-vegetation fire model, which assesses whether surrounding vegetation could be ignited by fires originating in buildings. The study also integrates well-established models, such as the Rothermel model, for vegetation fire spread and heat radiation calculations for the ignition of buildings. The model is validated using a real-world case study of the Getty Fire in California, USA, which occurred in 2019. The results are compared with actual fire spread data and simulations from the FlamMap6 software to evaluate the model's performance. [Results] The Getty Fire case study shows that the proposed multimodel combination method more accurately predicts fire spread than conventional single-model methods. The combination model effectively captures vegetation-building interactions, which are often ignored by traditional models. The burned area and fire perimeter are estimated with higher accuracy than FlamMap6, particularly in areas with mixed fuel types. The incorporation of thermal radiation calculations enhances ignition predictions, particularly in mixed vegetation and building areas, demonstrating the importance of modeling these interactions for higher accuracy. The model successfully predicts ignition timing for vegetation and buildings and dynamic changes in fire spread over time. Compared with FlamMap6, which uses lower grid resolution and lacks interaction modeling, the proposed combination model more precisely predicts fire behavior. FlamMap6 tends to overestimate fire spread in several areas, whereas the proposed method more accurately differentiates fire risks on the basis of fuel type. [Conclusions] The proposed extended multimodel combination method addresses the limitations of the existing fire spread models by incorporating distinct models for vegetation and buildings and accounting for their interactions in WUIs. This will improve our understanding of fire spread in complex environments. The results of this case study indicate that this method can provide critical support for emergency management through more accurate and timely prediction of fire spread. Future work should include further optimization, integrating firefighting interventions and more diverse fuel types, to enhance the model's applicability and efficiency in large-scale fire scenarios.

[Objective] This study aims to investigate the non-ideal rupture mechanism of burst discs within an explosion shock tube and its impact on the propagation characteristics of shock waves within the tube. Gaining a comprehensive understanding of the complex dynamics involved in burst disc rupture and its effect on shock-wave propagation is essential for enhancing the safety and reliability of explosion simulation devices, such as explosion shock tubes. These devices are vital tools in explosion damage research. [Methods] This study was conducted using a 13-meter-long explosion shock tube under varying explosion intensity conditions. High-speed strain and dynamic pressure acquisition methods were employed to determine the rupture time and pressure peak values corresponding to different burst disc opening ratios. High-speed photography and the Schlieren method were employed to observe the impact of varying opening ratios on downstream shock-wave propagation. A theoretical analysis of shock-wave flows was performed to develop a semi-empirical model that captured the differences in the Mach number within the downstream pipeline for various burst disc opening ratios. Furthermore, the attenuation rate of the incident shock wave was analyzed. By integrating rigid body fixed-axis deflection theory with the Drewry model, a high-precision prediction model was established to accurately determine the burst disc rupture angle under instantaneous explosion loads. [Results] The high-speed strain and overpressure measurements reveal that the burst disc rupture process unfolds in three distinct stages: the crack tip formation stage, petal evolution stage, and plastic completion stage. The crack tip formation stage plays a crucial role in affecting the pressure rise rate in the driving section. Meanwhile, the petal evolution and plastic completion stages determine the overpressure peak throughout the rest of the shock tube. Furthermore, a theoretical analysis of shock-wave flows leads to the development of a semi-empirical model describing the Mach number variations in the downstream pipeline corresponding to various burst disc opening ratios. By examining the attenuation rate of the incident shock in the experimental section, the propagation characteristics of shock waves are elucidated, leading to the construction of a quantitative model. The shock-wave attenuation rate transitions through three distinct phases. Initially, it increases gradually as the burst disc’s petal evolution stage impedes the flow, affecting both shock-wave generation and propagation. Subsequently, when A_ratio surpasses 0.2, the attenuation rate rises sharply, owing to reduced obstruction from the disc and increased dynamic loads in the driver section, leading to intensified shock-wave decay. Furthermore, once A_ratio exceeds 0.5, the attenuation rate stabilizes, indicating a dynamic equilibrium in shock-wave attenuation, which remains unaffected by further changes in A_ratio. Finally, by integrating rigid body fixed-axis deflection theory with the Drewry model, a high-precision prediction model for the burst disc rupture angle under instantaneous explosion loads is established, considerably reducing the model’s prediction error rate from 32.59% to 6.31%. [Conclusions] Overall, this study provides a robust experimental and theoretical framework for understanding the flow field evolution within a shock tube influenced by the non-ideal rupture effects of a burst disc. The findings substantially enhance the safety and reliability of explosion simulation devices, such as explosion shock tubes. The findings of this study have the potential to advance the structural design of explosion simulation devices and provide valuable insights into the evolution of flow fields influenced by non-ideal burst disc ruptures.

[Objective] This study aims to analyze the gait characteristics of firefighters during stair climbing under weight-bearing conditions to assess their impact on energy consumption. Firefighters frequently engage in high-intensity tasks, such as carrying heavy equipment upstairs in high-rise buildings, which can significantly affect their physical performance and increase fatigue. Understanding the intricate relationship between gait parameters and energy consumption in these strenuous conditions is crucial for optimizing training efficiency and improving operational efficiency in fire rescue actions. This study not only investigates gait changes owing to loads but also seeks to establish a predictive model to help in training and operational planning. [Methods] The study involved 24 healthy male subjects for two experiments. Experiment (A) performed stair climbing without additional weight, while Experiment (B) carried approximately 26.9 kg of firefighting gear, including protective clothing, a breathing apparatus, and a fire hose, simulating the typical loads that firefighters bear during emergencies. A 3-D gait analysis system captured motion data, including step frequency, step length, stride length, and overall energy consumption. The system also measured single- and double-leg support phases, cycle time, and other gait characteristics relevant to assessing performance under loads. Statistical analyses compared gait differences between weight-bearing and non-weight-bearing conditions, while correlation analyses identified relationships among energy consumption and specific gait parameters, highlighting factors significantly influencing fatigue. [Results] The results revealed that weight-bearing conditions led to significant changes in gait characteristics. Specifically, step frequency, step length, and stride length significantly reduced, indicating reduced movement efficiency when carrying heavy loads. Conversely, energy consumption, single-leg support time, double-leg support time, and cycle duration increased markedly, highlighting the added physical demands placed on firefighters. Correlation analysis indicated strong associations between energy consumption and several key gait parameters, including double-leg support time, cycle duration, speed, step length, and stride length. These findings suggest that specific gait adaptations occur in response to increased loads, which in turn affect overall energy consumption. To quantify these relationships, a support vector machine (SVM) model was developed to predict energy consumption using these gait parameters, achieving a high accuracy with R² of 0.858 36, thus confirming the model reliability. This result suggests that the SVM model is a reliable tool for estimating the energy consumption associated with weight-bearing stair climbing in firefighters. [Conclusions] The study highlights the considerable impact of weight-bearing stair climbing on firefighters' gait characteristics and energy consumption. Prolonged exposure to such strenuous tasks can lead to significant physical fatigue, increasing injury risks. The predictive SVM model developed in this study provides valuable insights into how specific gait parameters contribute to overall energy consumption, offering a robust foundation for optimizing firefighter training programs. By improving gait mechanics and reducing energy consumption during high-intensity tasks, this research enhances firefighter safety and performance in emergencies. Future research could explore the long-term effects of repeated weight-bearing activities on firefighter health and develop strategies to mitigate fatigue and injury risks, leading to improved training protocols that emphasize strength and endurance, ultimately benefiting firefighter well-being and operational readiness.

[Objective] Extreme precipitation events have increased globally in recent years, leading to more frequent and intense urban flooding that seriously threatens public safety. Reliable models for assessing storm waterlogging vulnerability and studies on spatial differentiation are essential for effective disaster prevention and mitigation. However, traditional models often face two main limitations. First, traditional models frequently overlook human adaptive responses to flooding and rely on single-weight calculation methods, reducing the accuracy of their insights. Second, traditional models generally apply to larger scales, such as cities or regions, and fail to capture smaller-scale spatial differences in vulnerability. This study introduces a refined storm waterlogging vulnerability assessment model that includes exposure, sensitivity, and coping capacity. The model allows researchers to reveal the spatial clustering of storm waterlogging vulnerability in more detail, providing more in-depth insights into areas most prone to flooding. [Methods] To establish a comprehensive assessment system, this study combined city-specific conditions with human adaptive responses. Nine key indicators, such as annual rainfall, were selected to capture urban-specific vulnerability. Subjective weights were assigned based on an improved expert scoring method, effectively incorporating expert insights. To enhance objectivity, the entropy method was used to calculate objective weights. Then, these subjective and objective weights were combined and optimized using the Nash equilibrium equation to achieve a balanced vulnerability evaluation. Multisource data and ArcGIS software enabled the visualization of storm waterlogging vulnerability on a 1 km grid scale in Xi'an. Global Moran's I and local indicators of spatial association (LISA) score clustering were used to analyze spatial patterns in storm waterlogging vulnerability, revealing clusters and trends across the city. In addition, a vulnerability triangle classified vulnerability levels into eight distinct types, highlighting dominant factors across regions and supporting targeted resilience planning. [Results] The vulnerability assessment showed that areas with high and relatively high vulnerability to urban storm waterlogging mainly clustered in the central old city within the Third Ring Road. This area primarily consisted of various functional zones with hard-paved surfaces, dense construction, intensive development, and high population density. In contrast, areas with low and relatively low vulnerability were mainly clustered in Chang'an, Huyi, Lintong, Yanliang, Zhouzhi, and Lantian Districts, which consisted mostly of forest and agricultural lands, providing high ecological resilience. LISA clustering analysis revealed that storm waterlogging vulnerability had a clear spatial clustering pattern with a significant positive spatial correlation. Eight storm waterlogging vulnerability types are identified: strongly integrated vulnerability (ESC), sensitivity-dominated (S), exposure and sensitivity-dominated (ES), sensitivity and coping capability dominated (SC), coping capability-dominated (C), exposure and coping capability-dominated (EC), weakly integrated vulnerability (O), and exposure-dominated (E) types. The ESC type mainly appeared in the northwest and northern areas of Xi'an; the S and ES types were concentrated within the central urban area; the SC types appeared around the city center; the C types were found in the eastern regions; the EC types occupied much of the southern area of the city; and the O and E types were less common and appeared sporadically in various locations. In lower vulnerability areas, the C types were predominant. In lower to moderate vulnerability areas, the EC types were the most common. In higher vulnerability areas, the SC and ESC types were the most frequent. [Conclusions] The proposed method provides a reliable scientific approach to assessing storm waterlogging vulnerability. It effectively visualizes quantitative data and identifies key factors influencing vulnerability across regions. The findings support the creation of storm waterlogging risk maps and indicate targeted disaster prevention and mitigation strategies.

[Objective] Hot events often lead to rampant online rumor spread. To prevent the incitement of public sentiment and the exacerbation of social contradictions, government departments must conduct timely and accurate situation assessment and response efficiency analysis before the outbreak of an online rumor crisis. In this regard, this paper investigates the trigger mechanism and congestion effects of online rumor crises. [Methods] By analyzing the evolution system of online rumors, a model for the trigger mechanism and congestion effects of online rumor crises is constructed using the improved susceptible exposed infectious recovered (SEIR) model and the stochastic Petri net (SPN). The constructed trigger model, SE(ER)IR-SPN, is refined by delineating the involved latent population group into exaggerators or rational spreaders. The equilibrium system state and precise trigger timing are obtained by analyzing transmission equilibrium points, trigger thresholds, and the density change trends of different characteristic groups. The congestion effects of emergency responses to crisis events after the outbreak of rumors are analyzed based on the busy rates of places and the utilization rates of transitions. Finally, the model applicability is verified using a medical and health event in City A as a case study. [Results] The research indicates that the SE(ER)IR-SPN model can detect high-risk online rumor events early, providing decision support for government departments during the disposal phase based on the busy rates of places and the utilization rates of transitions. The model effectively captures the dynamics of rumor spread and the subsequent congestion effects in emergency response processes. [Conclusions] The SE(ER)IR-SPN model is a valuable tool for the early identification of online rumor crises, enabling government departments to make informed decisions during the disposal phase. Detailed analysis of the model components, including the busy rates of places and the utilization rates of transitions, offers insights into the optimization of emergency response workflows. The case study considered herein confirms the practical utility of the model, highlighting the potential for broad application in managing and mitigating the impact of online rumor crises.