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This paper describes a newly developed software tool to evaluate human thermal safety and thermal comfort in cold-weather activities aimed at guiding users to arrange activity plans and select appropriate clothing ensembles. The software inputs include conditions of activity, environment, human body, and clothing ensemble. It outputs physiological temperatures, cold injury risks, thermal sensations, and thermal comforts in intuitive ways like cloud maps and curves. The software tool is characterized by (1) integration of a thermoregulatory model that predicts human thermophysiological responses under exercise conditions in cold environments, (2) the functions of clothing ensemble database and individual parameter database, (3) the human centric outputs that directly reflect human physiological and mental status, and (4) the user-friendly operation interface and output interface, as well as a wide applicability. The software is validated with human test studies covering ambient temperatures from - 30.6 to 5 °C, clothing ensembles from 1.34 to 3.20 clo, and activity intensities from 2 to 9 Mets. The average prediction RMSEs of core temperature, mean skin temperature, thermal sensation, and thermal comfort are 0.16 °C, 0.45 °C, 0.58, and 1.41, respectively. The software is an advanced expansion to current standards and guidance of cold exposure assessment and a meaningful tool for the fields of occupational health care, cold protection, and environmental ergonomics.

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 specific risk assessment of informal settlements (IS) is important for the management of IS and protection of environmental safety and public health. In this paper, we introduced the different types of IS in China, and conducted the fire risk assessment on 26 burning buildings in these IS, providing a semi-quantitative and scenario fire risk perception of IS in China for the readers. Two methods, the risk index and the Bayesian network, are proposed and adopted for the fire risk assessment in IS. First, a risk index system with a total of 69 factors is used to assess the degree of fire risk of buildings in IS semi-quantitatively, and the result shows that fire equipment and fire safety management on IS are seriously lacking. Then, a Bayesian network of building fire risk with a total of 66 nodes was established to assess the fire risk from ignition to spread as well as the safety evacuation. Overall, the possibility of ignition is high, but due to the role of fire equipment and fire protection design, the possibilities of fire from ignition to spread is gradually reduced. Finally, we also put forward some feasible suggestions for occupants in IS, community organizations and emergency managers to reduce the fire risk from the aspects of fire equipment and fire safety management.

In workplaces such as steel, power grids, and construction, firefighters and other workers often encounter non-uniform high-temperature environments, which significantly increase the risk of local heat stress and local heat discomfort for the workers. In this paper, a multi-segment human bioheat model is developed to predict the human thermal response in asymmetric high-temperature environments by considering the sensitivity of the modeling to angular changes in skin temperature and the effects of high temperatures on human thermoregulatory and physiological responses simultaneously. The extended model for asymmetric high-temperature environments is validated with the current model results and experimental data. The result shows that the extended model predicts the human skin temperature more accurately. Under non-uniform high-temperature conditions, the local skin temperature predictions are highly consistent with the experimental data, with a maximum difference of 2 °C. In summary, the proposed model can accurately predict the temperature of the human core and skin layers. It has the potential to estimate human physiological and thermoregulatory responses under uniform and non-uniform high-temperature environments, providing technical support for local heat stress and local thermal discomfort protection.

The exposure to airborne particles are of great concern in public health. The present study was aimed to clarify the effects of the breathing mode (nasal and oral inhalation) and exposure conditions on particle inhalation and deposition in human airway.A scanned upper airway embedded body model in an extended computational domain was constructed to perform numerical investigation into the inhalation and deposition of airborne particles.It was clarified that the inhalation of sub-micro and micro particles was of high efficiency and insensitive to the breathing mode, while super microparticles were much less inhalable, in particular under the nasal intake. Moreover, the relative variation of environmental wind speed and direction could significantly reshape the breathing zone as well as modify the critical inhalable area at far upstream. In addition, the breathing mode was found to be affective on the regional deposition of the microparticles, and increasing the proportion of nasal inhalation flowrate slightly enhanced the total deposition in the upper airway model.The breathing mode and exposure conditions significantly influence the particle inhalability and deposition pattern in human airway, which should be considered in the evaluation of health risk associated with airborne particle exposure.

A numerical human thermo-physiological model is developed with the consideration of characteristics of exercising people in cold environments. The developed model is characterized by: 1) the concept of net exercise efficiency which is used to correct the calculation of metabolic heat production by excluding mechanical energy; 2) the effects of low temperature on basal metabolic rate and basal blood flow rate; 3) the integration with a multi-layer clothing model to calculate the heat and moisture transfer through the clothing system, which takes into account the air gaps between the clothing layers to reflect the ventilation and air penetration effect from the ambient wind. Human subject experiment is conducted in a climate chamber to validate the proposed model. The human subject experiment is also carried out in a cold environment (-5 °C) combined with different air velocity conditions (still air, 2 m/s), taking into account the activities of different intensities (standing statically, 2 km/h walking and 7 km/h running). Thermo-physiological parameters including the core temperature, 8-point local skin temperatures and the clothing layer temperatures, are measured during the experiment. Comparison between the predicted and experimental results gives the root mean squared error (RMSE) of core temperature and mean skin temperature of 0.06-0.10 °C and 0.17-0.27 °C, respectively. RMSE values for local skin and clothing layer temperatures are no higher than 1.5 °C and most within 0.8 °C. The model is also validated with published data under various ambient temperature and activity intensity conditions. The proposed model is shown to be capable of predict the thermo-physiological responses of people exposed and exercising in cold environments.

Pedestrian dynamics in emergent situations such as stampedes and fire hazards have been widely studied, while few studies are focusing on human behavior in terrorist attack events. The research is vitally important for guiding efficient evacuation of the attacked crowd and reducing casualties. However, the research is insufficient due to the lack of researching materials. Besides, it is unconvincing for researchers to carry out these types of experiments. In this paper, the recorded video of the 3 ⋅ 1 Kunming terrorist attack event in China is analyzed. The instantaneous velocity field of the attacked crowd in the ticket hall is extracted from the video material, which is based on an image processing method. By analyzing the statistical characteristics of crowd velocity, crowd movement frequency and crowd entropy, several phenomena are found including crowd oscillating phenomenon, self-organizing grouping phenomenon. These discoveries will benefit the understanding of crowd dynamic in close-range attacks which is essential for the evacuation guidance efficiency, better design of architecture layout, and better defending strategies against the terrorists.

Simulating pedestrian flow through individual models is a powerful method to uncover mechanisms underlying collective behaviors. The heuristic force-based model is a promising approach, but it has problems both in theory and in application. Therefore, we propose a modified heuristic force-based model that considers contact force information and static friction in this paper. We propose a heuristic to take contact forces simultaneously as the information that affects self-driven forces, which has not been studied before. We express self-driven forces through static friction, because motion of pedestrians is actually dependent on the static friction between pedestrians and ground, which has not been considered before. Our model successfully addresses a well-known problem related to force-based models, namely the unrealistic backward motion of the last people repulsed by the preceding one. Besides, our model reproduces the bypass movement in a narrow space well, while the previous model will cause an...

The Accelerator Driven Subcritical Reactor System (ADS) is developed to transmute the nuclear wastes. Nevertheless, just like the traditional reactors, the ADS is faced with the problem of how to remove the decay heat efficiently. In order to solve the problem, a design of helium-cooled ADS with tungsten target is presented in this paper, and the multiplication characteristics of it are researched. The design of the subcritical core is based on the scheme of high temperature gas-cooled reactor, and to improve the ability of transmutation, the subcritical core is divided into two regions, which form up the fast-thermal coupling configuration. The parameters of fuel arrangement, position of target, height of active core, and height of top/bottom reflector are optimized to achieve the good physical and thermal performance. In the process of optimization, the influences of these parameters on the characteristics of the core are investigated through numerical experiments simulated by the MCNP6 code. The results indicate that the K eff and the K s may differ considerably for the gas-cooled ADS with fast-thermal coupling configuration. Moreover, we find that even with the same K eff , the K s of the subcritical core is different for similar schemes.

Rescue is a tough and time-consuming task in huge disaster. After stricken by a catastrophic natural disaster, the rescue force is typically deployed under the circumstance of traffic paralysis and lack of supplies. To cope with this situation, plenty of human resource and professional equipment are required. However, both the human resource and equipment are insufficient in most situations. The rescue force needs an efficient technology for searching the survivals, such as the life detection, which can be a key factor in disaster relief. Fast and accurate locating of the survivals will guide the limited rescue resource to save more survivors. However, current life detection technology cannot meet the demand of rescue in huge disaster. A novel life detection system based on fake base station is proposed. The detection system can scan the whole disaster area and find the cell phones, which leads to the surviving owners. As the system works in different way with other traditional human localization technology, a novel RSS/AOA hybrid positioning algorithm was proposed in this paper to increase the accuracy of localization. To verify the accuracy, two simulation tests were designed. The result of the two tests illustrates that the novel algorithm is more suitable to work with the novel life detection system.

A numerical study was conducted on the effects of ambient temperature and humidity on the transportation of sodium chloride particles (100 nm-1 μm) in a human airway model ranging from the nasal cavity to bronchi. A mucus-tissue structure was adopted to model the mass and heat transfer on the airway surface boundary. The temperature and humidity distributions of the respiratory flow were calculated and then the interaction between the particle and water vapor was further analyzed. It was predicted that the particle size grew to the ratio of 5–6 under subsaturation conditions because of hygroscopicity, which shifted the deposition efficiency in opposite directions on dependence of the initial particle size. However, the particles could be drastically raised to 40 times of the initial 100 nm diameter if the supersaturation-induced condensation was established, that was prone to occur under the cold-dry condition, and consequently promoted the deposition significantly. Such behavior might effectively contribute to the revitalized coronavirus disease 2019 (COVID-19) pandemic in addition to the more active virus itself in winter.

BackgroundTesting is one of the most effective means to manage the COVID-19 pandemic. However, there is an upper bound on daily testing volume because of limited healthcare staff and working hours, as well as different testing methods, such as random testing and contact-tracking testing. In this study, a network-based epidemic transmission model combined with a testing mechanism was proposed to study the role of testing in epidemic control. The aim of this study was to determine how testing affects the spread of epidemics and the daily testing volume needed to control infectious diseases.MethodsWe simulated the epidemic spread process on complex networks and introduced testing preferences to describe different testing strategies. Different networks were generated to represent social contact between individuals. An extended susceptible-exposed-infected-recovered (SEIR) epidemic model was adopted to simulate the spread of epidemics in these networks. The model establishes a testing preference of between 0 and 1; the larger the testing preference, the higher the testing priority for people in close contact with confirmed cases.ResultsThe numerical simulations revealed that the higher the priority for testing individuals in close contact with confirmed cases, the smaller the infection scale. In addition, the infection peak decreased with an increase in daily testing volume and increased as the testing start time was delayed. We also discovered that when testing and other measures were adopted, the daily testing volume required to keep the infection scale below 5% was reduced by more than 40% even if other measures only reduced individuals’ infection probability by 10%. The proposed model was validated using COVID-19 testing data.ConclusionsAlthough testing could effectively inhibit the spread of infectious diseases and epidemics, our results indicated that it requires a huge daily testing volume. Thus, it is highly recommended that testing be adopted in combination with measures such as wearing masks and social distancing to better manage infectious diseases. Our research contributes to understanding the role of testing in epidemic control and provides useful suggestions for the government and individuals in responding to epidemics.

Background: Testing is one of the most effective means to deal with epidemics. However, there is an upper bound on daily testing volume due to the limited healthcare staff and working hours, and different testing methods may also be adopted such as random testing and contact-tracking testing. Methods: In this paper, a network-based epidemic transmission model combined with testing mechanism is proposed to study the role of testing in epidemic control. We simulate the epidemic spread process on complex networks and introduce testing preference to describe different testing strategies. Results: Through a series of numerical simulations, we find that testing can flatten the infection curve and delay the outbreak of epidemics. In addition, the higher the priority for testing individuals in close contacts with confirmed cases, the smaller the infection scale. Compared with the increase speed of daily testing volume, the upper bound of daily testing volume plays a more important role in epidemic control. We also discover that when testing combined with other measures is adopted, the daily testing volume required to control epidemics (i.e., control infection scale below 5%) will be reduced by more than 40% even if other measures only reduce individuals’ infection probability by 10%. Conclusions: In short, although testing can effectively inhibit the spread of infectious diseases, it requires a huge amount of daily testing volume. It is highly recommended that testing be adopted combined with other measures such as wearing masks and social distancing to deal with infectious diseases. Our research contributes to understanding the role of testing in epidemic control and provides useful suggestions for the government and individuals in response to epidemics.

The present numerical study investigated the transportation time of the inhaled chemicals in three realistic human airway models by adopting a methodology from the field of the building ventilation. Two indexes including “scale of ventilation efficiency 3 (SVE3)” and “local purging flow rate (L-PFR)” were used to evaluate the respective arrival time and staying time under different inhalation flow rates. The general trend of the SVE3 was predicted as expected and the exceptions within the nasal cavities were attributed to the uneven allocation of the inhaled flow between the internal channels and the formation of the vortex circulation therein. The complicated situation of the L-PFR was also explained by the structure constrains. Moreover, the variation of the two indexes with the flow rate was sensitive to the inter-subjective differences but the distribution pattern was not changed significantly. By combining the SVE3 and L-PFR, it could help with assessing the potential effect of the inhaled chemicals on the human health for engineering applications to which the relative impacts are more interested than the absolute value. But for the precise evaluation regarding a specific chemical, comprehensive simulation is still necessary with the surface adsorption included under realistic respiration cycles.

The Accelerator Driven Subcritical Reactor System (ADS) has a strong ability to transmute minor actinides (MAs) and it has inherent safety because of its subcritical core. However, the removal of decay heat is a major problem for the ADS and the design of spallation target is still a difficulty. Therefore, this paper presents a design of gas-cooled ADS with passive safety to attain the good performance of removing decay heat and transmutation. For the design of the target, the helium-cooled tungsten target is decided to adopt, and it is modelled into the cone structure to strengthen the convective heat transfer. A series of numerical experiments have been made to determine the optimal parameters of the target using the MCNP and CFX code. In the design of core, the subcritical core is divided into fast neutron region which the MAO2 is placed in and thermal neutron region which the UO2 is placed in to improve the performance of the transmutation. Two methods are proposed in this paper for the thermal-hydraulic calculation in the steady-state analysis, and the results show its good thermal performance.

There have generally been two kinds of approaches to the empirical study of human mobility. At the group level, some valuable information might be submerged in statistical noise, while due to the diversity of individual purpose and preference, there is still no general statistical regularity of human mobility at the individual level. In this paper, we considered group-level human mobility as the combination of several basic patterns and analyzed the collective mobility by category. Utilizing matrix factorization and correlation analysis, we extracted some of the stable/occasional components from the collective human mobility in the Beijing subway and found that the departure and arrival mobility patterns have different characteristics, both in time and space, under various conditions. We classified individual records into different patterns and analyzed the most likely trip distance by category. The proposed method can decompose stable/occasional mobility patterns from the collective mobility and identify passengers belonging to different patterns, helping us to better understand the origin of different mobility patterns and provide guidance for emergency management of large crowds.

Indoor fire water supply system is an important guarantee for building fire safety, and the design parameters such as water flow and water pressure need to meet the requirements of fire prevention regulations. Based on the monitoring data of the Internet of Things (IoT), this paper took an office building as an example to analyze the water pressure variation trend of the building's indoor fire water supply system, and then predict it based on the historical water pressure drop rate fitting and Long short-term memory (LSTM) method. It is found that the variation of water pressure is periodic, and this trend was analyzed from the perspective of physical structure of the indoor fire water supply system. Furthermore, due to the regularity of water pressure variation, the prediction results are generally good. With historical water pressure drop rate fitting, the prediction accuracy is relatively higher, and the abnormal change points of water pressure can be discovered, but the water pressure value needs to be reset for further prediction; while the LSTM method is self-adaptive, when the frequency of water pressure monitoring is high and the amount of water pressure data is large, the LSTM is more suitable.

The individual behavior model is a contributory factor to improve the accuracy of agent-based simulation in different scenarios. However, few studies have considered moving threat agents, which often occur in terrorist attacks caused by attackers with close-range weapons (e.g., sword, stick). At the same time, many existing behavior models lack validation from cases or experiments. This paper builds a new individual behavior model based on seven behavioral hypotheses. The driving-forces model is an extension of the classical social force model considering scenarios including moving threat agents. An experiment was conducted to validate the key components of the model. Then the model is compared with an advanced Elliptical Specification II social force model, by calculating the fitting errors between the simulated and experimental trajectories, and being applied to simulate a specific circumstance. Our results show that the driving-forces model reduced the fitting error by an average of 33.9% and the standard deviation by an average of 44.5%, which indicates the accuracy and stability of the model in the studied situation. The new driving-forces model could be used to simulate individual behavior when analyzing the risk of specific scenarios using agent-based simulation methods, such as risk analysis of close-range terrorist attacks in public places.

Pedestrian behavior models have successfully reproduced human movement in many situations. However, few studies focus on modeling human behavior in the context of terrorist attacks. Terrorist attacks commonly occur in crowded public areas and result in a large number of casualties. This paper proposes a three-stage model to reproduce a series of complex behaviors and decision-making processes at the onset of an attack, when pedestrians generally do not have clear targets and have to deal with fuzzy information from the attack. The first stage of the model builds a Bayesian belief network to represent the pedestrians’ initial judgment of the threat and their evacuation decisions. The second stage focuses on pedestrians’ global assessment of the situation through an analogy with diffusion processes. The third stage uses a cost function to reproduce the trade-offs of distance, safety, and emotional impact when considering a path to take. The model is validated using a video from the November 2015 Paris attack. The behavioral characteristics and trajectories of three pedestrians extracted from the video are reproduced by the simulation results based on the model. The research can be used to set rules when performing risk analysis and strategic defensive resource allocation of terrorist attacks using agent-based simulation methods.

In disaster relief, especially in the rescue of serious disaster like great earthquake or tsunami, detecting the exact location of survivors via portable devices can greatly improve the efficiency of search and rescue work. A novel infrastructure-free cell phone positioning system based on software-defined radio, virtual base transceiver station software and its running scheme is proposed in this paper. The identification information of cell phones, receive signal strength and signal transceiver power data can be collected by this system. By using customized antennas with new positioning method, the proposed system can indicate the location of survivals trapped under debris without any operation on cell phones by survivors. With the new hybrid algorithm, search radius of the system can reach more than 1.2 km. And it is also able to implement accurate positioning by this system at the same time. Experimental results demonstrated that the system has significantly improved performance over the traditional methods.

The suppression effects of ultrafine Mg (OH)2 powders with different particle sizes and mass fractions on explosion flame of wood dust are experimentally studied in a half-closed vertical experimental duct. Flame structures and characteristic parameters, including flame light emission images, propagation velocity, temperature, during the flame propagation of wood dust explosion are recorded by high-speed photography and fine thermocouple. Thermal decomposition behaviors of wood dust and Mg(OH)2 powders are studied using synchronous thermal analyzer. Chemical structures of residual dust samples after the explosion are characterized by Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. The experimental results show that explosion flame of wood dust is obviously suppressed by physical and chemical effects of Mg(OH)2 powders, and the suppression effect of nano-Mg(OH)2 is better than that of micron-Mg(OH)2 under same mass fractions. By analyzing multiple characteristics of nano-powders, the advantages of nano-Mg(OH)2 over micron-powders are further investigated.

Individuals' preventive measures, as an effective way to suppress epidemic transmission and to protect themselves from infection, have attracted much academic concern, especially during the COVID-19 pandemic In this paper, a reinforcement learning-based model is proposed to explore individuals' effective preventive measures against epidemics Through extensive simulations, we find that the cost of preventive measures influences the epidemic transmission process significantly The infection scale increases as the cost of preventive measures grows, which means that the government needs to provide preventive measures with low cost to suppress the epidemic transmission In addition, the effective preventive measures vary from individual to individual according to the social contacts Individuals who contact with others frequently in daily life are highly recommended to take strict preventive measures to protect themselves from infection, while those who have little social contacts do not need to take any measures considering the inevitable cost Our research contributes to exploring the effective measures for individuals, which can provide the government and individuals useful suggestions in response to epidemics © 2021 Chinese Physical Society and IOP Publishing Ltd

Heterogeneous parallel computing is being widely used for particle transport Monte Carlo codes on various platforms. However, there is currently no related research on the Sunway TaihuLight system, a heterogeneous supercomputer with a unique architecture. In this study, RMC, a key particle transport Monte Carlo code, was parallelized for use on Sunway TaihuLight. Some optimization methods including a thread-private keff calculator mechanism based on Pthreads were introduced into RMC. The criticality computing module of the optimized RMC was then implemented on Sunway TaihuLight with an MPI-Athread parallel model and optimization methods oriented to the system’s special architecture. The results were verified by comparison with the original RMC. The criticality calculation is 125.3 times faster with 512 core groups each with 65 cores than the original serial RMC on 1 core on an Intel Xeon X5670. Stable weak scalability of the parallel calculation is shown with over 1 million cores.

Artificial intelligence has been successfully applied in many areas including forensic sciences. Perhaps all forensic works can be regarded as helping reconstruct crimes, i.e. clarify and sequence the events that took place in the commission of a crime through evidence. However, there are few researches on the crime reconstruction using artificial intelligence methods. In this paper, we present a model based on Bayesian networks to help solve crimes. The model, which is termed ‘case-type based model’, is based on the knowledge of a type of crimes. We use Bayesian networks to represent the knowledge and conduct the uncertainty reasoning. We propose a growth algorithm of Bayesian networks to adapt the model to different cases. The model was tested through a real case, and the results indicate that the model can provide effective investigation suggestions and achieve the crime reconstruction.

In recent years, various types of terrorist attacks occurred, causing worldwide catastrophes. According to the Global Terrorism Database (GTD), among all attack tactics, bombing attacks happened most frequently, followed by armed assaults. In this article, a model for analyzing and forecasting the conditional probability of bombing attacks (CPBAs) based on time-series methods is developed. In addition, intervention analysis is used to analyze the sudden increase in the time-series process. The results show that the CPBA increased dramatically at the end of 2011. During that time, the CPBA increased by 16.0% in a two-month period to reach the peak value, but still stays 9.0% greater than the predicted level after the temporary effect gradually decays. By contrast, no significant fluctuation can be found in the conditional probability process of armed assault. It can be inferred that some social unrest, such as America's troop withdrawal from Afghanistan and Iraq, could have led to the increase of the CPBA in Afghanistan, Iraq, and Pakistan. The integrated time-series and intervention model is used to forecast the monthly CPBA in 2014 and through 2064. The average relative error compared with the real data in 2014 is 3.5%. The model is also applied to the total number of attacks recorded by the GTD between 2004 and 2014.

We studied the distribution of entry time interval in Beijing subway traffic by analyzing the smart card transaction data, and then deduced the probability distribution function of entry time interval based on the Maximum Entropy Principle. Both theoretical derivation and data statistics indicated that the entry time interval obeys power-law distribution with an exponential cutoff. In addition, we pointed out the constraint conditions for the distribution form and discussed how the constraints affect the distribution function. It is speculated that for bursts and heavy tails in human dynamics, when the fitted power exponent is less than 1.0, it cannot be a pure power-law distribution, but with an exponential cutoff, which may be ignored in the previous studies.

Menezes and Barabasi have once developed a framework by mean–variance analysis to investigate the relationship between the average flux and fluctuations and found a scaling-law behavior in two distinct phase. By then most related researches dedicated to understanding the scaling laws through the internal/external forces of the system. However, as for subway system, the demand of transporting a large amount of passengers during peak hours is a necessity. Thus, alternating the phase or reducing the impact of external forces by cutting down the total passengers is completely unrealistic. The more operable approaches are specifically regulating the distribution of passenger flow in order to drive the system into a more stable phase. In this paper, we carried out the mean–variance analysis on subway traffic and dedicated to discover the effect of spatial–temporal distributions of passenger flow on the fluctuations in subway traffic. By analyzing the control properties of the transit networks constructed according to different passenger flow threshold, it is found that the control mode of transit networks might differ the phase change of fluctuations, which proposed a new perspective of controlling the fluctuations in subway traffic.

Crime is pervasive all around the world. Understanding the influence of social features on crime occurrences of a city is a hot topic among researchers. Correlations between crime and other social characteristics have been studied by large amounts of statistical models, including Ordinary Least Square (OLS) linear regression model, Random Forest (RF) regression model, Artificial Neural Network (ANN) model and so on. However, results of these studies, such as the prediction accuracy, are not satisfying and many contradictory conclusions are achieved in previous research works. These controversies are triggered by several factors, including the non-Gaussian distributions and multicollinearity of urban social data, inaccuracy and inadequacy of the processed data, etc. To fill these gaps, we analyzed the influence of 18 urban indicators within 6 categories including geography, economy, education, housing, urbanization and population structure on crime risk in China’s major prefecture-level cities by year. We used the big data algorithm, Least Absolute Shrinkage and Selection Operator (LASSO) and Extremely-randomized Trees (Extra-Trees), to predict the crime risk and quantify the influence of urban parameters on crime. 83% of accuracy on crime risk prediction can be obtained from our fitted model and the importance of urban indicators is ranked. Results show that area of land used for living, number of subscribers of mobile telephone, employed population are the three main factors on the crime occurrences in China. Our research makes contributions to better understanding of the effects of urban indicators on crime in a socialist nation, and providing instructions and strategies for crime prediction and crime rate control with governments, in this big-data era.

Information dissemination has drawn increasing attention of scholars and the government for its importance to disaster response. Understanding the dynamics of information dissemination can help the government disseminate information effectively. Considering people tend to share information with the intimate under disaster, social distance which measures the intimacy between individuals is introduced in this paper. An information dissemination model with preference selection based on social distance is also proposed. Through extensive simulations, we discover the information dissemination process will be suppressed if people prefer disseminating information to individuals with short social distance. In order to facilitate the information dissemination, award to spreaders is found useful for its significant acceleration in dissemination speed. In addition, we examine the efficiency of strategies the government adopts to disseminate information. Comparing to inform-way (q fraction of nodes informed in descending order of H-index), the government is recommended to disseminate information by broadcast-way (all nodes accepting information with a probability λ b ). Our work contributes to understanding the dynamics of information dissemination, and provides the government with suggestions to disseminate information effectively under disaster.

Pedestrian dynamics has many significant applications, such as the evacuation and the design and optimization of pedestrian facilities. The most important characteristic of pedestrian dynamics is the fundamental diagram, which indicates the relationship between density, velocity and flow of pedestrians. The fundamental diagram varies for different facilities like corridors, stairs or ramps, and it also depends on whether the pedestrian flow is unidirectional or bidirectional. However, the general model for the fundamental diagram is unknown. Here we use a decay model to quantify the relationship between the walking speed and density. The concept ‘maximum density’, which was thought previously as the density where pedestrians hardly move, is corrected to the density where the ‘turbulent’ crowd conditions arise. The decay characteristic of pedestrian movement is found, which is seldom studied before. The decay model agrees well with empirical results, and it can interpret the disagreement of different fundamental diagrams quantitatively.

After the damage of Fukushima Daiichi Nuclear Power Plant, a great number of radioactive materials were released into the Pacific Ocean. Therefore, it is necessary to research on the temporal and spatial distribution of these radionuclides. We use Princeton Ocean Model to simulate the circulation of the coast water of Fukushima NPP and obtain the concentration of caesium-137 by solving the diffusion equations. We employ the Monte Carlo N-particle (MCNP) code to assess the external doses caused by these contaminated sea water. To improve the efficiency and effectiveness of volume source in MCNP code, we establish a transformation method between spot source and volume source, and determine an appropriate range of volume source. Finally, we calculate the absorbed doses of every organ/tissue and the effective dose of a human body.

The prevention and treatment of epidemic is always an urgent problem faced by the human being. Due to the special space structure, huge passenger flow and great people mobility, the subway lines have become the areas with high epidemic transmission risks. However, there is no recent study related to epidemic transmission in the subway network on urban-scale. In this article, from the perspective of big data, we study the transmission risk of epidemic in Beijing subway network by using urban subway mobility data. By reintegrating and mining the urban subway mobility data, we preliminary assess the transmission risk in the subway lines from the passenger behaviors, station features, route features and individual case on the basis of subway network structure. This study has certain practical significance for the early stage of epidemic tracking and prevention.

An arson attack brought great social reflection in Xiamen, June 2013. In this arson, 47 people died and 34 injured on a bus. Arson is a unique category of intentional attack that results in grave consequence once occurs. This paper establishes an arson assessment model based on the particular characteristic of arson, using the risk analysis methods. 8803 arson attacks from 2000 to 2011 in Global Terrorism Database are studied. Threat, vulnerability, consequence and risk aiming at different target types are compared, so as to provide technical support against arson for defensive arrangement and macro emergency decision.