Crime script analysis as a methodology to analyse criminal processes is underdeveloped. This is apparent from the various approaches in which scholars apply crime scripting and present their cybercrime scripts. The plethora of scripting methods raise significant concerns about the reliability and validity of these scripting studies. In this methodological paper, we demonstrate how object-oriented modelling (OOM) could address some of the currently identified methodological issues, thereby refining crime script analysis. More specifically, we suggest to visualise crime scripts using static and dynamic modelling with the Unified Modelling Language (UML) to harmonise cybercrime scripts without compromising their depth. Static models visualise objects in a system or process, their attributes and their relationships. Dynamic models visualise actions and interactions during a process. Creating these models in addition to the typical textual narrative could aid analysts to more systematically consider, organise and relate key aspects of crime scripts. In turn, this approach might, amongst others, facilitate alternative ways of identifying intervention measures, theorising about offender decision-making, and an improved shared understanding of the crime phenomenon analysed. We illustrate the application of these models with a phishing script.
MULTIFILE
The changing climate has an effect on the quality of life in our cities: heavier rainfall (resulting infloodings), longer periods of drought, reduced air and water quality and increasing temperatures incities (heat stress). Awareness about these changes among various stakeholders is of greatimportance. Every Dutch region is required to perform a stresstest indicating the effects of climatechange (o.a. flooding and heatstress) before 2020. The level of execution, area size and level ofparticipation of stakeholders, has intentionally been made flexible.To provide more insight into the approaches and best management practices to climate resilience,this article provides 3 examples of stresstests performed on several levels: single object real estatelevel, city level and national district level. The method ‘stresstestíng’, involves flood and heatstressmodeling, defines the current status of climate adaptation characteristics of an object, city or district.The stresstest form the base line and starting point for the national 3 step approach adaptationstrategy ‘analyse, ambition and action’.The 3 pilots have been evaluated as ‘successful’ by stakeholders and yielded a significant amount ofvaluable information, further improvement is recommended as increasing the participation of theprivate sector, in a ‘quadruple helix approach’. The learning points from these 3 examples ofstresstests will subsequently be implemented in the form of improved stresstesting in the nearfuture in (inter)national cities around the world.
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Accurate modeling of end-users’ decision-making behavior is crucial for validating demand response (DR) policies. However, existing models usually represent the decision-making behavior as an optimization problem, neglecting the impact of human psychology on decisions. In this paper, we propose a Belief-Desire-Intention (BDI) agent model to model end-users’ decision-making under DR. This model has the ability to perceive environmental information, generate different power scheduling plans, and make decisions that align with its own interests. The key modeling capabilities of the proposed model have been validated in a household end-user with flexible loads
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