Understanding particle deposition patterns in the pulmonary acinus is essential for early intervention and treatment in acinar diseases. This study numerically investigated the effects of respiratory modes and emphysematous alveolar wall ablation on airflow and particle deposition in a physiologically representative pulmonary acinar model. A heterogeneous acinar model was developed, incorporating alveolar expansion and contraction via the dynamic meshing method, and its validity was confirmed by comparison with published particle deposition data. Airflow and particle transport patterns were then analyzed under varying respiratory modes and degrees of alveolar wall ablation. For particles smaller than 1 μm, deposition decreased with higher breathing frequency and increased with larger tidal volume. Smaller particles penetrated deeper and deposited more uniformly due to strong airflow coupling. Compared with the normal acinus, the lesioned acinus exhibited reduced airflow variability, lower expansion capacity, and a decreased deposition fraction. Alveolar wall ablation impaired lung expansion and restricted distal airflow penetration, leading to localized particle deposition near the acinar entrance. As lesion severity increased, the deposition progressively declined due to altered flow patterns and a reduced surface-to-volume ratio. The particle deposition declined nonlinearly with lesion severity. A 30% wall ablation reduced total deposition by over 40%, whereas further increases to 60% and 90% caused only minor additional decreases, indicating a nonlinear response in which early structural damage disproportionately affects acinar particle deposition. These findings underscore the importance of early intervention to preserve alveolar drug deposition efficiency and improve therapeutic outcomes in patients with progressive pulmonary diseases such as emphysema.

The profound uncertainty surrounding emerging infectious diseases, as exemplified by the cross-seasonal dynamics of COVID-19, poses significant challenges to conventional forecasting efforts. Traditional models often rely heavily on granular population flow data, limiting their applicability across city, national, and global scales. This study aims to shift the paradigm from deterministic prediction to multi-scale scenario projection. We propose an extended SEQIHRS compartmental model integrating key epidemic drivers. The core innovation is the introduction of a space-induced immunity factor ( k_0 ), which characterizes transmission barriers across geographical scales without requiring explicit mobility data. A periodic skewness function captures asymmetric seasonal effects, and a composite intervention framework simulates adaptive control measures. Three empirical datasets from Hong Kong, Mainland China, and the Global scale​ were utilized to calibrate and validate the model's structural plausibility. The model successfully reproduced the observed epidemic trends across diverse spatiotemporal settings. The close alignment between simulated curves and surveillance data demonstrates that the model can capture complex transmission patterns under various parameterizations, confirming its robustness as a scenario-generating engine. Compared to traditional approaches, this framework offers three breakthroughs: (1) It achieves seamless multi-scale simulation​ (local to global) via the k_0 factor, bypassing the need for complex population flow data; (2) It incorporates uncertainty through interpretable parameters, allowing users to explore a range of plausible outcomes rather than a single forecast; (3) It provides a practical tool for pandemic preparedness. By adjusting pathogen parameters, spatial scales, and intervention intensities, policymakers can conduct "what-if" analyses to support adaptive emergency responses.

Accurate non-invasive estimation of core temperature is essential for real-time assessment of heat strain. However, direct measurements are often invasive or unsuitable for continuous wearable monitoring, while existing estimation approaches remain constrained by predefined parameters, model assumptions, and limited adaptability to dynamic conditions. This study developed a bioheat model-based extended Kalman filter (Bio-EKF) that integrates an improved two-node bioheat model with extended Kalman filter-based data assimilation, using heart rate and skin temperature as non-invasive observations for state correction. The proposed Bio-EKF was evaluated using experimental data collected under transient thermal conditions with varying ambient temperature and activity intensity. Its performance was compared with a conventional two-node bioheat model (Bio-G), a modified two-node bioheat model (Bio-M), and a heart-rate-based EKF model (HR-EKF). Robustness was further assessed under measurement-loss scenarios and simplified skin-temperature sensing configurations. The proposed Bio-EKF achieved best agreement with measured core temperature among the evaluated approaches, with an RMSE, MAE, and bias of 0.43 °C, 0.31 °C, and -0.16 °C, respectively. The framework maintained acceptable accuracy during simultaneous loss of heart rate and skin temperature measurements for up to 40 min (RMSE = 0.53 °C, MAE = 0.38 °C, Bias = -0.11 °C). Comparable performance was also achieved using a single upper-arm temperature measurement (RMSE = 0.42 °C, MAE = 0.29 °C, Bias = -0.14 °C). These results demonstrate the potential of the proposed Bio-EKF framework for continuous and interpretable core-temperature estimation using wearable physiological observations under realistic monitoring conditions.

This paper explores the challenges of controlling complex metro systems, which are influenced by uncertain and uncontrollable large passenger flow impacts. Traditionally, flow-limiting measures during peak periods have been based on experience rather than scientific theory. To bridge this gap, we introduce a novel network analysis method inspired by control centrality theory. This approach assesses the impact of traffic loads from single or multiple sources on any node within the metro network. Our method provides a scientific basis for operators to develop policies for managing overloaded traffic, enhancing both safety and efficiency in metro system operations.

An Eulerian model combined with population balance equation was developed in this study to investigate the coagulation and deposition of polydisperse particles in the human respiratory tract. The mass and moment terms were incorporated into the model to capture the size-dependent particle dynamics such as inertial drift and diffusion deposition. Experiments were conducted using a three-dimensional (3D) printed human upper airway cast under different particle number concentration conditions. The simulation results reached a fair well agreement with the measurement data. The validated model was then applied to analyze the effect of coagulation on a sub micrometer particle size change and deposition fraction. It was predicted that the higher number concentration and longer residence time promoted particle coagulation. A fitting equation for predicting cigarette smoke particle size and number distribution as a function of residence time was provided. Over 90% of particle mass loss in the airway model was attributed to coagulation, with less than 10% due to deposition. The total deposition fraction of cigarette smoke particles was decreased, as the formation of larger particles from coagulation reduced the diffusion effect. However, regional deposition in the larynx was increased due to enhanced inertial impaction. The numerical method provided in this study addressed the limitations of semi-empirical or analytical formulas for deposition prediction, enabling coupled Eulerian simulations of coagulation and deposition in a three-dimensional respiratory tract model. It can also be extended to explore the effects of other aerosol physics that involved size changes, such as particle breakup and growth on the airway deposition within this framework.

The present numerical study aimed to clarify the effects of exposure conditions and human movement on cough droplets transmission and deposition. Two human manikins stood face-to-face were constructed within a computational domain, and the dynamic mesh method was used to realize the human movement. A validated computational fluid-particle dynamic model was adopted to simulate the transport, evaporation, and deposition of droplets emitted by human coughs under varied upstream velocities and relative humidities. The findings revealed a significantly higher percentage of droplets deposited on body surfaces compared to those inhaled into the airway. Specifically, the ratio between these two fractions was approximately 5 under a calm air environment, increasing to around 20 in windy conditions due to reduced inhalation and enhanced body surface deposition at higher wind speeds. The high humidity inhibited droplet evaporation and facilitated droplet sedimentation on the ground, consequently decreasing droplets reaching the susceptible person and mitigating associated health risks. Moreover, the impact of increased distance between the two virtual humans on reducing exposure risk was found to be exponentially diminished under windy conditions. In the coughing human movement scenario, droplets deposited on the susceptible human body and inhaled into the airway increased to 1.54 and 1.66 times, respectively, under the static condition, as the induced airflow pushed the droplets closer to the susceptible human body and respiratory zone.

Fire is one of the most serious threats faced by immovable cultural heritage (ICH). In recent years, multiple ICH fires have occurred worldwide, exposing the shortage in fire protection work. It is largely due to insufficient attention on city-scale fire protection governance (FPG). This study aims to provide the FPG framework and strategies through a case analysis of the city with an extraordinary number of ICH sites. Beijing is selected as the case city, which has never experienced an ICH fire accident since 2010. The fire vulnerability drivers (FVDs), facing challenges, and contents of FPG plans are analyzed to clarify the lessons learned from past accidents and current countermeasures. It is found that there are diverse factors that can increase the fire occurrence probability and firefighting difficulty, including the inherent attributes, diversified uses, and restricted locations of ICH sites. There are also multiple challenges in FPG activities. However, Beijing has already formulated a series of response plans which highlight the significant role of institutionalized, source-oriented, technological, and socialized governance. These plans can be summarized as "Beijing mode". It may serve as an example of how city-scale FPG can be implemented in practice. This study also reminds people that FPG is a global task always on the road. It needs the efforts and experience from all countries to improve the current situation.

Understanding how drivers perceive and respond to external stimuli in driving tasks is important for the development of advanced driving technologies and human-computer interaction. In this paper, we conducted a temporal response analysis between driving data and cortical activation data measured by functional near-infrared spectroscopy (fNIRS), based on a naturalistic driving experiment. Temporal response function analysis indicates that stimuli, which elicit significant responses of drivers include distance, acceleration, time headway, and the velocity of the preceding vehicle. For these stimuli, the time lags and response patterns were further discussed. The influencing factors on drivers' perception were also studied based on various driver characteristics. These conclusions can provide guidance for the construction of carfollowing models, the safety assessment of drivers and the improvement of advanced driving technologies.

Timely and accurate radiation dose assessment is essential for effective emergency response in nuclear accidents. However, meteorological uncertainties, especially in wind data, can lead to substantial discrepancies between simulated and observed plume behaviors, compromising situational awareness and decision-making. This study proposed a physics-informed optimization framework that integrates a physical radiation assessment model with a genetic algorithm to dynamically correct time-series wind field data and mitigate discrepancies caused by meteorological uncertainty. The physical model couples the Lagrangian puff model with the point kernel integration method. To improve efficiency, a dimensionality reduction approach simplifies the three-dimensional gamma dose integration to one dimension. The proposed framework was validated using the first venting scenario of Unit 1 at the Fukushima Daiichi Nuclear Power Plant. The temporal optimization significantly enhanced the alignment of estimated and observed plume passage times. Quantitatively, the optimization respectively reduces the fractional bias (FB) and the normalized mean square error (NMSE) at the Main Gate by 57.82 % and 90.69 %, while the improvements at MP8 station reached 97.88 % (FB) and 92.19 % (NMSE). The FAC2 (Fraction of predictions within a factor of two) at the Main Gate increased substantially from 9.5 % to 52.4 % post-optimization. These improvements demonstrate the effectiveness of the proposed method in enhancing predictive accuracy for emergency radiation dose assessment and optimizing operational decision-making under complex atmospheric conditions.

The content and methods of traditional safety training can no longer meet the needs of the industry or accident-targeted safety training. To solve this problem, this study proposes an accidental case data accident causing model-driven safety training method (ACDACM method) based on an analysis of the content and methods of China's safety training. This method is theoretically driven by the accident causation model and data-driven by the accident case information. Through data analysis of historical accident cases, basic information and comprehensive reasons for accidents can be obtained. Using data mining algorithms, the causes and combinations of high-frequency and high-risk accidents can be mined, supported by accident data, and a path map of the accident causes can then be obtained. The developed safety training project is more targeted by comparing accident causes with industry standards and regulations. This study considers coal and gas outburst accidents as an example and uses 24Model to analyze 84 coal and gas outburst accidents in China. The targeted safety training plan had eight parts of training content, and supporting PPTs, handbooks, and videos were produced. The application shows that this method can provide targeted safety training designs for industries or accidents and has industry universality. Finally, the advantages and development of the ACDACM method are elaborated and the future of safety training is discussed. This study provides theoretical and methodological support for targeted safety-training programs.

Purpose The present study assesses the impact resistance of the shear thickening fluids-filled (STFs-filled) foam through drop-hammer impact tests. Design/methodology/approach The maximum residual impact load and specific impact energy absorption rate of STF-filled foam are studied with varying thickness (4–14 mm), densities (0.35–0.6 g/cm3) and hardness (40–50 Rockwell Hardness C Scale (HRC)) under different ambient temperatures (−20−20 °C) and impact energies (25–75 J). Findings The following conclusions are obtained from this study: (1) the higher the impact energy, the greater the maximum residual impact force and energy absorption efficiency of the material; (2) the impact resistance of STF-filled foam can be improved with the decrease of ambient temperature, achieving the highest energy absorption rate at −10?. (3) STF-filled foam substrate has the highest impact resistance, the lowest maximum residual impact force and the highest energy absorption coefficient when the density is 0.35 g/cm3, the hardness is 45HC and the thickness is 10 mm. Originality/value This is the first paper to analyze the impact of both environmental factors and material properties on the impact resistance of STF-filled foam. The results show that the decrease in temperature and the increase in hardness can enhance the impact resistance of STF-filled foam.

The traditional storage method of fire accident cases is mainly in the form of text, and it is difficult to effectively conduct comprehensive analysis due to the limited ability to display key information and fire knowledge. In this paper, a structured storage form of building fire cases was proposed based on knowledge graph, which can comprehensively describe and visualize the fire causes, the dynamic fire development process and evacuation process. It enables readers to get information and knowledge from building fire cases intuitively, and supports the comprehensive analysis for building fire prevention strategies. The knowledge graphs are constructed for two common building types (residential and public buildings), and have the capacity to reflect the dynamic development law of fires from ignition to spread in different buildings. Meanwhile, as the occupants’ evacuation is the first concern when a fire occurs, the knowledge graphs also visualize the relationship among various conditions in the evacuation process. Different application scenarios are displayed in the paper, including case query, root-cause analysis and consequence forecasting, which shows the advantages and applicability of building fire knowledge graph.

Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design. Lane formation, a typical self-organizing phenomenon, helps pedestrian system to become more orderly, the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world. In this study, a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed, and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed. Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation. A high tendency of following results in good lane formation. Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease. The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70% of his own. The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model. The presence of a small obstacle does not obstruct the walking of pedestrians; in contrast, it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.

Precipitation from tropical cyclones (TCs) can cause massive damage from inland floods and is becoming more intense under a warming climate. However, knowledge gaps still exist in changes of spatial patterns in heavy TC precipitation. Here we define a metric, DIST30, as the mean radial distance from centers of clustered heavy rainfall cells (> 30 mm/3 h) to TC center, representing the footprint of heavy TC precipitation. There is significant global increase in DIST30 at a rate of 0.34 km/year. Increases of DIST30 cover 59.87% of total TC impact areas, with growth especially strong in the Western North Pacific, Northern Atlantic, and Southern Pacific. The XGBoost machine learning model showed that monthly DIST30 variability is majorly controlled by TC maximum wind speed, location, sea surface temperature, vertical wind shear, and total water column vapor. TC poleward migration in the Northern Hemisphere contributes substantially to the DIST30 upward trend globally.

为评估核事故后放射性物质大气扩散带来的安全健康风险,以福岛核事故为例进行分析.基于开发的拉格朗日-欧拉耦合模型,模拟计算放射性物质扩散和干湿沉降过程,利用预测评估系统开发大尺度放射性危险物质在大气中的扩散烟云、地面辐射剂量以及基于呼吸和肺部沉积的内照射剂量评估模型,实现了基于耦合模型的中大尺度风险评估.结果表明:福岛核事故对太平洋和东北亚地区带来了一定的辐射风险;基于耦合模型开发了中大尺度风险评估方法,并对福岛核事故进行了辐射风险评估,可为应急决策提供数据支持,为未来类似事件的风险评估提供参考.

Pedestrian self-organizing movement has played a significant role in evacuation studies and architectural design. For lane formation, a typical self-organizing phenomenon that helps pedestrian system to get more ordered, the majority of following and overtaking behavior models are imprecise and unrealistic compared with pedestrian movement in the real world. In this study, a pedestrian dynamic model considering detailed modelling of the following and overtaking behaviors is constructed, and a measure of lane formation and pedestrian system order based on information entropy is proposed. Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation. A high tendency of following results in good lane formation. Both non-selective following behavior and aggressive overtaking behavior cause decrease of system order. The most orderly following strategy for a pedestrian is to overtake former pedestrian whose speed is lower than approximately 70% of their own. The influence of the obstacle layout on pedestrian lanes and egress efficiency is also studied with this model. The presence of a small obstacle may not obstruct the walking of pedestrians; in contrast, it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.

In modern safety management, it is very important to study the influence of the whole safety system on unsafe acts in order to prevent accidents. However, theoretical research in this area is sparse. In order to obtain the influence law of various factors in the safety system on unsafe acts, this paper used system dynamics simulation to carry out theoretical research. First, based on a summary of the causes of the coal and gas outburst accidents, a dynamic simulation model for unsafe acts was established. Second, the system dynamics model is applied to investigate the influence of various safety system factors on unsafe acts. Third, the mechanism and the control measures of unsafe acts in the enterprise safety system are studied. This study’s main result and conclusions are as follows: (1) In the new coalmines, the influence of the safety culture, safety management system, and safety ability on the safety acts were similar. The order of influence on the safety acts in production coalmines is as follows: safety management system > safety ability > safety culture. The difference is most evident in months ten to eighteen. The higher the safety level and safety construction standard of the company, the greater the difference. (2) In the construction of the safety culture, the order of influence was as follows: safety measure elements > safety responsibility elements = safety discipline elements > safety concept elements. It shows the difference in influence from the 6th month and attains its maximum value from the 12th month to the 14th month. (3) In the construction of the safety management system, the degree of influence in new coalmines was as follows: safety policy > safety management organization structure > safety management procedures. Among them, especially in the first 18 months, the impact of the safety policy was most apparent. However, in the production mine, the degree of influence was as follows: safety management organization structure > safety management procedures > safety policy, but the difference is very small. (4) The degree of influence on the construct of safety ability was as follows: safety knowledge > safety psychology = safety habits > safety awareness, but the difference on the impact was small.

The traditional research of building fire probability analysis is from statistics or fire science. This paper combines the two methods and aims to improve the statistical method of building ignition probability determination according to the research conclusion of fire science. The specific factors that affect the ignition probability are divided into three aspects: humans, ignition sources and combustibles and environments. On this basis, the Bayesian network of building ignition probability is constructed, the nodes and conditional probability table in the Bayesian network are introduced in detail, according to which the ignition probability of building can be calculated quantitatively and objectively. Then some typical buildings are chosen as examples for the application of the method, the posterior probability value is calculated by obtaining the relevant building information and substituting them into the Bayesian network. The ignition probability is dynamic, and the comparison with the statistical data of building fire also proves its rationality.