Design of Multi Agent Based Crowd Injury Model

A major concern of many government agencies is to predict and control the behavior of crowds in different situations. Many times such gatherings are legal, legitimate, and peaceful. But there are times when they can turn violent, run out of control, result in material damages and even casualties. It then becomes the duty of governments to bring them under control using a variety of techniques, including non-lethal and lethal weapons, if necessary. In order to aid decision makers on the course of action in crowd control, there are modeling and simulation tools that can provide guidelines by giving programmed rules to computer animated characters and to observe behaviors over time in appropriate scenarios. A crowd is a group of people attending a public gathering, with some joint purpose, such as protesting government or celebrating an event. In some countries these kinds of activities are the only way to express public\u27s displeasure with their governments. The governments\u27 reactions to such activities may or may not be tolerant. For these reasons, such situations must be eliminated by recognizing when and how they occur and then providing guidelines to mitigate them. Police or military forces use non-lethal weapons (NLWs), such as plastic bullets or clubs, to accomplish their job. In order to simulate the results of such actions in a computer, there is a need to determine the physical effects of NLWs over the individuals in the crowd. In this dissertation, a fuzzy logic based crowd injury model for determining the physical effects of NLWs is proposed. Fuzzy logic concepts can be applied to a problem by using linguistic rules, which are determined by problem domain experts. In this case, a group of police and military officers were consulted for a set of injury model rules and those rules were then included in the simulation platform. As a proof of concept, a prototype system was implemented using the Repast Simphony agent based simulation toolkit. Simulation results illustrated the effectiveness of the simulation framework

A quality assessment framework for knowledge management software

CONTEXT: Knowledge is a strategic asset to any organisation due to its usefulness in supportinginnovation, performance improvement and competitive advantage. In order to gain the maximum benefit from knowledge, the effective management of various forms of knowledge is increasingly viewed as vital. A Knowledge Management System (KMS) is a class of Information System (IS) that manages organisational knowledge, and KMS software (KMSS) is a KMS component that can be used as a platform for managing various forms of knowledge. The evaluation of the effectiveness or quality of KMS software is challenging, and no systematic evidence exists on the quality evaluation of knowledge management software which considers the various aspects of Knowledge Management (KM) to ensure the effectiveness of a KMS.AIM: The overall aim is to formalise a quality assessment framework for knowledge management software (KMSS).METHOD: In order to achieve the aim, the research was planned and carried out in the stages identified in the software engineering research methods literature. The need for this research was identified through a mapping study of prior KMS research. The data collected through a Systematic Literature Review (SLR) and the evaluation of a KMSS prototype using a sample of 58 regular usersof knowledge management software were used as the main sources of data for the formalisation of the quality assessment framework. A test bed for empirical data collection was designed and implemented based on key principles of learning. A formalised quality assessment framework was applied to select knowledge management software and was evaluated for effectiveness. RESULTS: The final outcome of this research is a quality assessment framework consisting of 41 quality attributes categorised under content quality, platform quality and user satisfaction. A Quality Index was formulated by integrating these three categories of quality attributes to evaluate the quality of knowledge management software.CONCLUSION: This research generates novel contributions by presenting a framework for the quality assessment of knowledge management software, never previously available in the research. This framework is a valuable resource for any organisation or individual in selecting the most suitable knowledge management software by considering the quality attributes of the software