Abstract

Abstract

Abstract
This study proposes a benchmarking approach to assess water supply and sanitation services' performance in Brazilian municipalities from an integrated perspective of efficiency and access. The regional differences and the impact of the governance models adopted by different municipalities (local public entities, regional public entities, and private entities) on efficiency levels are also explored. The results revealed significant heterogeneity in the efficiency levels of Brazilian municipalities, as the average efficiency score is relatively low (45%). The Southeast and Center-west regions stand out both in terms of efficiency and access dimensions. There is evidence that municipalities with services provided by local public entities have higher efficiency than those with regional providers. On the other hand, efficiency differences between municipalities with public and private providers are statistically significant. Finally, regulatory strategies are suggested based on the outcome of the integrated analysis.

Abstract
Geolocation data identifies the geographic location of people or objects, which may unveil the performance of some activity or operation. A good example is, if a vehicle is in a gas station then one may assume that the vehicle is being refuelled. This work aims to obtain vehicle-based operations from geolocation data by analysing the stationary states of vehicles, which may identify some motionless event (e.g. bus line stops and traffic incidents). Ultimately, these operations may be analysed with Process Mining techniques in order to discover the most significant ones and extract process related information. In this work, we studied the application of diverse approaches for detecting vehicle-based operations and identified different operations related to the bus services. The operations were also characterized according the distribution of their events, allowing to identify specific operations characteristics. The public transport network of Rio de Janeiro is used as a case study, which is supported by a real-time data stream of buses geolocations.

Abstract
As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.

Abstract
Serious Games apply game strategies to a training environment to encourage participants to make decisions and face challenges - the more interactive, the greater the participants' involvement with the content. Moreover, the best way to train is to simulate and identify scenarios for decision making, recreating situations, and strategies for learning. The Serious Games for training have this purpose. A Serious Game for training can be refined with a game narrative, a methodology centered on group experience defining problems and giving solutions through the game story. The challenge is how to diversify a unique narrative according to the individual player's experience. The present study aims to answer, using Design Science Research, whether a personalized narrative can improve the design of serious games for training. The specific goal is to design, develop and evaluate an artifact based on Design Thinking to design a personalized narrative method for Serious Games.