On The Incorporation Of The Personality Factors Into Crowd Simulation

Recently, a considerable amount of research has been performed on simulating the collective behavior of pedestrians in the street or people finding their way inside a building or a room. Comprehensive reviews of the state of the art can be found in Schreckenberg and Deo (2002) and Batty, M., DeSyllas, J. and Duxbury, E. (2003). In all these simulation studies, one area that is lacking is accounting for the effects of human personalities on the outcome. As a result, there is a growing emphasis on researching the effects of human personalities and adding the results to the simulations to make them more realistic. This research investigated the possibility of incorporating personality factors into the crowd simulation model. The first part of this study explored the extraction of quantitative crowd motion from videos and developed a method to compare real video with the simulation output video. Several open source programs were examined and modified to obtain optical flow measurements from real videos captured at sporting events. Optical flow measurements provide information such as crowd density, average velocity with which individuals move in the crowd, as well as other parameters. These quantifiable optical flow calculations provided a strong method for comparing simulation results with those obtained from video footage captured in real life situations. The second part of the research focused on the incorporation of the personality factors into the crowd simulation. Existing crowd models such as HelbingU-Molnar-Farkas-Vicsek (HMFV) do not take individual personality factors into account. The most common approach employed by psychologists for studying personality traits is the Big Five factors or dimensions of personality (NEO: Neuroticism, Extroversion, Openness, Agreeableness and Conscientiousness). In this research forces related to the personality factors were incorporated into the crowd simulation models. The NEO-based forces were incorporated into an existing HMFV simulated implemented in the MASON simulation framework. The simulation results were validated using the quantification procedures developed in the first phase. This research reports on a major expansion of a simulation of pedestrian motion based on the model (HMFV) by Helbing, D., I. J. Farkas, P. Molnár, and T. Vicsek (2002). Example of actual behavior such as a crowd exiting church after service were simulated using NEO-based forces and show a striking resemblance to actual behavior as rated by behavior scientists

Smart performance optimization of energy-aware scheduling model for resource sharing in 5G green communication systems

This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy-aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.© 2024 The Authors. The Journal of Engineering published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Corrosion Inhibition of Mild Steel in 1 mol L−1 HCl Using Gum Exudates of Azadirachta indica

The ability of gum exudates of Azadirachta indica (GAI) to inhibit corrosion on mild steel in 1 mol L−1 HCl has been studied using mass loss, polarization, and impedence measurements. The effect of temperature (303–323 K) and immersion time of 1, 2, 4, 6, and 12 hours on corrosion behavior of mild steel was examined. Gum exudates decrease the corrosion rate up to a concentration of 80 ppm and at 323 K temperature. GAI adsorb chemically onto the surface of the mild steel while it obeys Langmuir adsorption isotherms. Polarization studies show GAI as mixed mode inhibitor. Surface studies ascertain that a shielding layer was formed on the mild steel surface

Incorporating Big Five Personality Factors Into Crowd Simulation

The simulation and modeling of crowd behavior has become an active research area in recent years. This area of research has been applied to a wide variety of domains such as military, education, training, entertainment and human factors analysis. Most crowd simulations do not consider the effects of cultural or personality diversity within the crowd. We incorporate these effects by modifying the social force terms within the Helbing-Molnar-Farjas-Vicsek (HMFV) crowd model implemented within the MASON (Multi-Agent Simulation of Neighborhoods) environment. The modification are based on the Big Five personality factors (neuroticism, extroversion, openness, agreeableness and conscientiousness) which have been found to be applicable across cultures. In addition to detailing the modifications, this paper reports on comparison of the Big Five modifications of the HMFV model to videos of crowds. An expert panel of behavioral scientists found the modified HMFV simulations to be realistic models. In addition a preliminary version of a technique used based on optical flow analysis of the videos showed good correlation

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