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The purpose of this paper is to analyze the crowd stampede risk mechanism from the perspective of systems thinking.

Causal loop diagram is drawn to outline the non-linear interactions among complex factors across the whole system and dissect the contributory factors of crowd stampede accident. To systematically construct the theoretical framework and find fundamental solutions, co-word analysis with Citespace is used to get the critical data. An agent-based simulation using Pathfinder is conducted to develop a spatial model for the Shanghai Stampede Accident that happened in 2014.

The causal loop diagram is formed to not only illustrate the symptomatic solutions with a quick fix but also dissect the fundamental solutions through an underlying systemic analysis. The simulation shows that crowd stampede experiences an interactive process of accumulation, trigger, delay, break and diffusion of risk factors within the crowd system. A linkage effect among the multidimensional characters of individuals and the system accelerates the stampede risk deterioration. There exists delay of the result of effect from the deep-level measure.

A top-down approach is offered to policymakers for crowd stampede risk protocol design and synergic emergency control that may reduce the risk of the stampede.

In this study, SDFT paradigm is proposed as the critical solution for the crowd stampede accident. In addition, a chain effect of energy and a linkage effect within the crowd system is illustrated for in-depth understanding of crowd stampede risk.

The purpose of this paper is to examine the manner in which advocates of crowdsourcing reconfigure the classical sociological treatment of the crowd.

The approach taken conceives of the semantics of crowd theorizing in three phases, each of which makes sense of the power dynamics between the elite and the crowd. In phases one and two, the crowd is conceptualized as a problem generator; in phase three, the crowd is depicted as a problem solver and innovator.

This paper provides a critical look at phase three crowd theorizing. It explores how, by ignoring the disruptive power dynamic, crowdsourcing generates a credible image of the crowd as an innovator and problem solver. The work concludes with a discussion of the implications of phase three crowd theorizing for researchers in sociology.

Advocates of the wisdom of crowds, if interested in the sociological implications of their position, must attend to both the disruptive and costly implications of third phase crowd theorizing.

This paper maps the crowdsourcing process and places it in context. It argues that the distance between the classical social scientific treatment of the crowd is not nearly as great as crowdsourcing advocates would have one believe. Nevertheless, phase three crowd theorizing opens up sociologically relevant questions regarding the future portrayal of collective intelligence as a form of virtual property.

The empirical analysis dealt in this paper emphasizes on the impact of military expenditures on out of pocket (OOP) healthcare payments. A sizeable body of defence economics literature has investigated the trade-off between military and public health expenditure, by testing the crowding-out or growth-stimulating hypothesis; does military expenditure scaling up crowd-out or promote governmental resources for social and welfare programs, including also state health financing?

In this study, panel data from 2000 to 2018 for 129 countries is used to examine the impact of military expenditure on OOP healthcare payments. The dataset of countries is categorized into four income-groups based on World Bank's income-group classification. Dynamic panel data methodology is applied to meet study objectives.

The findings of this study indicate that military expenditure positively affects OOP payments in all the selected groups of countries, strongly supporting in this way the crowding-out hypothesis whereby increased military expenditure reduces the public financing on health. Study econometric results are robust since different and alternative changes in specifications and samples are applied in our analysis.

Under the economic downturn backdrop for several economies in the previous decade and on the foreground of a potential limited governmental fiscal space related to the Covid-19 pandemic adverse economic effects, this study provides evidence that policy-makers have to adjust their government policy initiatives and prioritize Universal Health Coverage objectives. Consequently, the findings of this study reflect the necessity of governments as far as possible to moderate military expenditures and increase public financing on health in order to strengthen health care systems efficiency against households OOP spending for necessary healthcare utilization.

Despite the fact that a sizeable body of defence economics literature has extensively examined the impact of military spending on total and public health expenditures, nevertheless to the best of our knowledge there is no empirical evidence of any direct effect of national defence spending on the main private financing component of health systems globally; the OOP healthcare payments.

The purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm BP-prediction and reciprocal velocity obstacle (PRVO) combines the BP neural network for velocity PRVO to accomplish dynamic collision avoidance.

This presented method exhibits innovation by anticipating ahead velocities using BP neural networks to reconstruct the velocity obstacle region; determining the optimized velocity corresponding to the robot’s scalable radius range from the error generated by the non-holonomic robot tracking the desired trajectory; and considering acceleration constraints, determining the set of multi-step reachable velocities of non-holonomic robot in the space of velocity variations.

The method is validated using three commonly used metrics of collision rate, travel time and average distance in a comparison between simulation experiments including multiple differential drive robots and physical experiments using the Turtkebot3 robot. The experimental results show that our method outperforms other RVO extension methods on the three metrics.

In this paper, the authors propose navigation algorithms capable of adaptively selecting the optimal speed for a multi-robot system to avoid robot collisions during dynamic crowded interactions.

This paper aims to explore the development and application of place crowd safety management tools for areas of public assembly and major events, from a practitioner perspective.

The crowd safety risk assessment model is known as design, information, management-ingress, circulation, egress (DIM-ICE) (Still, 2009) is implemented to optimise crowd safety and potentially throughput. Three contrasting case studies represent examples of some of the world’s largest and most challenging crowd safety projects.

The paper provides some insight into how the DIM-ICE model can be used to aid strategic planning at major events, assess potential crowd risks and to avoid potential crowd safety issues.

It provides further clarity to what effective place management practice is. Evidence-based on the case studies demonstrates that the application of the DIM-ICE model is useful for recognising potential place crowd safety issues and identifying areas for require improvement.

Crowd science is an emerging field of research, which is primarily motivated by place crowd safety issues in congested places; the application and reporting of an evidence-based model (i.e. DIM-ICE model) add to this. The paper addresses a research gap related to the implementation of analytic tools in characterising place crowd dynamics.

In recent years, terrorist attacks have gradually become one of the important factors endangering social security. In this context, this research aims to propose methods and principles which can be utilized to make effective evacuation plans to reduce casualties in terrorist attacks.

By analyzing the statistical data of terrorist attack videos, this paper proposes an extended cellular automaton (CA) model and simulates the panic evacuation of the pedestrians in the terrorist attack.

The main findings are as follows. (1) The panic movement of pedestrians leads to the dispersal of the crowd and the increase in evacuation time. (2) Most deaths occur in the early stage of crowd evacuation while pedestrians gather without perceiving the risk. (3) There is a trade-off between escaping from the room and avoidance of attackers for pedestrians. Appropriate panic contagion enables pedestrians to respond more quickly to risks. (4) Casualties are mainly concentrated in complex terrains, e.g. walls, corners, obstacles, exits, etc. (5) The initial position of the attackers has a significant effect on the crowd evacuation. The evacuation efficiency should be reduced if the attacker starts the attack from the exit or corners.

In this research, the concept of “focus region” is proposed to depict the different reactions of pedestrians to danger and the effects of the attacker’s motion (especially the attack strategies of attackers) are classified. Additionally, the influences on pedestrians by direct and indirect panic sources are studied.

The purpose of this paper is to analyze the relationship between investment in public sector institutions and private investment in the Saudi economy by using Structural VAR model for testing the dynamic crowding‐out effect during the last four decades. Three fundamental variables are mobilized: the GDP, public investment and private investment. The linear relationship between structural shocks, which have an economic and financial interpretation, and the reduced random residuals has been established to evaluate the dynamic impacts. The findings show that the investment of the public sector institutions has an impact on the investment of private sector, and that the impulse response functions to the supply and demand shocks indicate that the crowding‐out effect is verified in the short and long run.

The purpose of this study is to analyze the crowd dynamics of the visitors at Al-Masjid al-Nabawi during the most oversaturated period to characterize the most critical conditions and suggest technical solutions to accommodate visitors and provide them safe passage.

In this study, the time of entrance from the Al-Salam Gate to the tomb and from the tomb to the exit from the Al-Baqi’ Gate has been collected in the most oversaturated period. To be precise and to model the worst case, important crowd measures of effectiveness data are collected in the two holiest times considered by Muslims, during the holy month of Ramadan and the month of Dhul-Hijjah and during the busiest hours of the day to consider safety factors while proposing future solutions. The conventional manual head-counting method has been adopted to determine the crowd density and to carry out actual counting of the visitors from the recorded videos and photos captured by the legitimate authority.

The analyses revealed that the crowd dynamics in the month of Ramadan (peak) are statistically different from those for other times (off peak). In general, the crowd dynamics at all times on days other than Ramadan are almost identical.

The results of crowd characterization from this study are expected to help optimize crowd management in the Masjid at the most critical location and time. The data collected in this study could be used for future research to simulate similar crowd scenes or for even different crowd management scenarios in case of emergencies such as fire hazards or evacuation process.

The purpose of this paper is to propose a theoretical framework of applying the Internet of Things (IoT) technologies to the intelligent evacuation protocol in libraries at emergency situations.

The authors conducted field investigations on eight libraries in Wuhan, China, analyzed the characteristics of crowd gathering in libraries and the problems of the libraries’ existing evacuation plans. Therefore, an IoT-based intelligent evacuation protocol in libraries was proposed. Its basic structure consisted of five components: the information base, the protocol base, the IoT sensors, the information fusion system and the intelligent evacuation protocol generation system. In the information fusion system, Dempster–Shafer (D-S) evidence theory was employed as the information fusion algorithm to fuse the multi-sensor information at multiple time points, so as to reduce the uncertainty of disaster prediction. The authors also conducted a case study on the Library L in Wuhan, China. A specific evacuation route was generated for a fire and the crowd evacuation was simulated by the software Patherfind.

The proposed IoT-based evacuation protocol has four distinguishing features: scenario corresponding, precise evacuation, dynamic correction and intelligent decision-making. The case study shows that the proposed protocol is feasible in practice, indicating that the IoT technologies have great potential to be successfully applied to the safety management in libraries.

The software and hardware requirements as well as the Internet network requirements of IoT technologies need to be further discussed.

The proposed IoT-based intelligent evacuation protocol can be widely used in libraries, which is one of the inspirations for the use of IoT technologies in modern constructers.

The application of IoT technologies in libraries is a brand-new topic that has drawn much attention in academia recently. The crowd safety management in libraries is of great significance, and there is little professional literature on it. This paper proposes an IoT-based intelligent evacuation protocol, aiming at improving the safety management in libraries at emergency situations.

This study seeks to examine what theory of crowd psychology is being applied within public order police training in England and Wales and what accounts of crowds, police strategies and tactics subsequently emerge among officers who undertake this training.

The study uses a multi‐method approach including observations of public order training courses, interviews with students and instructors, and the dissemination of questionnaires.

The analysis suggests that a form of crowd theory associated with the work of Gustave Le Bon has become institutionalised within police training. This in turn is leading to a potentially counter‐productive reliance on the undifferentiated use of force when policing crowds.

The study illustrates that such training outcomes not only are counter to the recent developments in evidence, theory and policy but also undermine the police's ability to develop more efficient and effective approaches to policing crowds.

The study provides a systematic review of public order training which demonstrates how crowd theory is used as a rationale and justification for the use of tactics based on undifferentiated force. It makes suggestions for improving police training so that updates in policy and theory can be translated into operational practice.