Abstract
Simulation modeling is a well-known and recurrent approach to study the performance of urban systems. Taking into account the recent and continuous transformations within increasingly complex and multidimensional cities, the use of simulation tools is, in many cases, the only feasible and reliable approach to analyze such dynamic systems. However, simulation models can become very time consuming when detailed input-space exploration is needed. To tackle this problem, simulation metamodels are often used to approximate the simulators' results. In this paper, we propose an active learning algorithm based on the Gaussian process (CP) framework that gathers the most informative simulation data points in batches, according to both their predictive variances and to the relative distance between them. This allows us to explore the simulators' input space with fewer data points and in parallel, and thus in a more efficient way, while avoiding computationally expensive simulation runs in the process. We take advantage of the closeness notion encoded into the GP to select batches of points in such a way that they do not belong to the same highvariance neighborhoods. In addition, we also suggest two simple and practical user-defined stopping criteria so that the iterative learning procedure can be fully automated. We illustrate this methodology using three experimental settings. The results show that the proposed methodology is able to improve the exploration efficiency of the simulation input space in comparison with non-restricted batch-mode active learning procedures.

Abstract
Points of Interest (POI) are widely used in many applications nowadays mainly due to the increasing amount of related data available online, notably from volunteered geographic information (VGI) sources. Being able to connect these data from different sources is useful for many things like validating, correcting and also removing duplicated data in a database. However, there is no standard way to identify the same POIs across different sources and doing it manually could be very expensive. Therefore, automatic POI matching has been an attractive research topic. In our work, we propose a novel data-driven machine learning approach based on an outlier detection algorithm to match POIs automatically. Surprisingly, works that have been presented so far do not use data-driven machine learning approaches. The reason for this might be that such approaches need a training dataset to be constructed by manually matching some POIs. To mitigate this, we have taken advantage of the Crosswalk API, available at the time we started our project, which allowed us to retrieve already matched POI data from different sources in US territory. We trained and tested our model with a dataset containing Factual, Facebook and Foursquare POIs from New York City and were able to successfully apply it to another dataset of Facebook and Foursquare POIs from Porto, Portugal, finding matches with an accuracy around 95%. These are encouraging results that confirm our approach as an effective way to address the problem of automatically matching POIs. They also show that such a model can be trained with data available from multiple sources and be applied to other datasets with different locations from those used in training. Furthermore, as a data-driven machine learning approach, the model can be continuously improved by adding new validated data to its training dataset. © Springer Nature Switzerland AG 2018.

Abstract
Mobility has become one of the most difficult challenges that cities must face. More than half of world’s population resides in urban areas and with the continuously growing population it is imperative that cities use their resources more efficiently. Obtaining and gathering data from different sources can be extremely important to support new solutions that will help building a better mobility for the citizens. Crowdsensing has become a popular way to share data collected by sensing devices with the goal to achieve a common interest. Data collected by crowdsensing applications can be a promising way to obtain valuable mobility information from each citizen. In this paper, we study the current work on the integrated mobility services exploring the crowdsensing applications that were used to extract and provide valuable mobility data. Also, we analyze the main current techniques used to characterize urban mobility. © Springer Nature Switzerland AG 2019.

Abstract
Industrial Symbiosis (IS) envisages a collaborative approach to resource efficiency, encouraging companies to recover, reprocess and reuse waste within the industrial network. Several challenges regarding the effective application of IS continue to limit a broader implementation of this area of Industrial Ecology. The MAESTRI project encompasses an Industrial Symbiosis approach within the scope of sustainable manufacturing for process industries that fosters the sharing of resources (energy, water, residues, etc.) between different processes of a single company or between multiple companies. The Industrial Symbiosis approach is integrated with Efficiency Framework in the so-called MAESTRI Total Efficiency Framework. Efficiency Framework is devoted to the combination of eco-efficiency (via ecoPROSYS) and the efficiency assessment (via MSM – Multi-Layer Stream Mapping). In this manuscript the benefit of the combination of the Efficiency Framework as an facilitator to a more effective application of Industrial Symbiosis, within or outside the company’s boundaries, is explored. © 2018 Taylor & Francis Group, London, UK.

Abstract
Nowadays achieving sustainable development is a global concern. Economic and environmental sustainability can be driven by assessing and improving industrial production system’s performance. An evaluation that assesses if materials, energy and resources are used to their full potential is a powerful tool for improving economic and environmental performance, and consequently supports the identification of all types of waste and inefficiencies along the production system. The goal of this work is to assess overall production system’s efficiency and eco-efficiency using Multi Layer Stream Mapping (MSM). The outputs of this approach is used to scrutinize “where” and “how much” can a unit process and/or a production system improve its financial, environmental and overall efficiency, thereby being of great importance for decision-making and correct implementation of improvement actions. This paper highlights the results from the application of the MSM methodology in a real industrial case regarding a painting unit.

Abstract
Industrial Symbiosis (IS) envisages a collaborative approach to resource efficiency, encouraging companies to recover, reprocess and reuse waste within the industrial network. Several challenges regarding the effective application of IS continue to limit a broader implementation of this area of Industrial Ecology. The MAESTRI project encompasses an Industrial Symbiosis approach within the scope of sustainable manufacturing for process industries that fosters the sharing of resources (energy, water, residues, etc.) between different processes of a single company or between multiple companies. The Industrial Symbiosis approach is integrated with Efficiency Framework in the so-called MAESTRI Total Efficiency Framework. Efficiency Framework is devoted to the combination of eco-efficiency (via ecoPROSYS) and the efficiency assessment (via MSM - Multi-Layer Stream Mapping). In this manuscript the benefit of the combination of the Efficiency Framework as an facilitator to a more effective application of Industrial Symbiosis, within or outside the company's boundaries, is explored.