Abstract
A smart city is a sustainable and efficient urban centre that provides a high quality of life to its inhabitants through optimal management of its resources. Energy management is one of the most demanding issues within such urban centres owing to the complexity of the energy systems and their vital role. Therefore, significant attention and effort need to be dedicated to this problem. Modelling and simulation are the major tools commonly used to assess the technological and policy impacts of smart solutions, as well as to plan the best ways of shifting from current cities to smarter ones. This paper reviews energy-related work on planning and operation models within the smart city by classifying their scope into five main intervention areas: generation, storage, infrastructure, facilities, and transport. More-complex urban energy models integrating more than one intervention area are also reviewed, outlining their advantages and limitations, existing trends and challenges, and some relevant applications. Lastly, a methodology for developing an improved energy model in the smart-city context is proposed, along with some additional final recommendations.

Abstract
In this work FZL and FPL fabricated using Focused Ion Beam milling on the top of custom-made optical fibers are presented. Primary, single mode fibers are spliced to a segment of multimode fiber allowing to expand the core region. Subsequently, FZL and FPL with several focusing distances are milled on the top of the fibers. In this regard, the zone and phase plates offer distinct focusing characteristics which are here presented and analyzed. Moreover, the output optical intensity field of the FPL and FZP are evaluated and validated using an implementation of the Finite Differences Time Domain (Lumerical). Lastly, some considerations on the use of the tips as fiber optical tweezers are given.

Abstract
The reuse of treated wastewater could be a promising measure to attenuate the water scarcity burden. In agriculture, irrigation with wastewater may contribute to improve production yields, reduce the ecological footprint and promote socioeconomic benefits. However, it cannot be considered exempt of adverse consequences in environmental and human health. Apart from the introduction of some biological and chemical hazardous agents, the disturbance of the indigenous soil microbial communities and, thus, of vital soil functions impacting soil fertility may occur. The consequences of these disturbances are still poorly understood. This chapter summarises the physicochemical and microbiological alterations in soil resultant from irrigation with treated wastewater that are described in scientific literature. These alterations, which involve a high complexity of variables (soil, wastewater, climate, vegetal cover), may have impacts on soil quality and productivity. In addition, possible health risks may arise, in particular through the direct or indirect contamination of the food chain with micropollutants, pathogens or antibiotic resistance determinants. The current state of the art suggests that irrigation with treated wastewater may have a multitude of long-term implications on soil productivity and public health. Although further research is needed, it seems evident that the analysis of risks associated with irrigation with treated wastewater must take into account not only the quality of water, but other aspects as diverse as soil microbiota, soil type or the cultivated plant species. © 2016 Springer International Publishing Switzerland.

Abstract
In this paper we address the problem of maximizing the expected number of transplants in a kidney exchange program. We propose an integer programming model with an exponential number of decision variables which are associated with cycles. By introducing the concept of type of cycle, we avoid the complete cycle enumeration and develop a branch-and-price approach. © 2016 Elsevier B.V.

Abstract
Service robots frequently face similar tasks. However, they are still not able to share their knowledge efficiently on how to accomplish those tasks. We introduce a new framework, which allows remote and heterogeneous robots to share instructions on the tasks assigned to them. This framework is used to initiate tasks for the robots, to receive or provide instructions on how to accomplish the tasks, and to ground the instructions in the robots' capabilities. We demonstrate the feasibility of the framework with experiments between two geographically distributed robots and analyze the performance of the proposed framework quantitatively.

Abstract
In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts and uses different training sets, each containing one more observation than the previous one. All possible state space models and all ARIMA models where the orders are allowed to range reasonably are fitted considering raw data and log-transformed data with regular differencing (up to second order differences) and, if the time series is seasonal, seasonal differencing (up to first order differences). The value of root mean squared error for each model is calculated averaging the one-step forecasts obtained. The model which has the lowest root mean squared error value and passes the Ljung-Box test using all of the available data with a reasonable significance level is selected among all the ARIMA and state space models considered. The procedure is exemplified in this paper with a case study of retail sales of different categories of women's footwear from a Portuguese retailer, and its accuracy is compared with three reliable forecasting approaches. The results show that our procedure consistently forecasts more accurately than the other approaches and the improvements in the accuracy are significant.