Abstract

Abstract
This chapter discusses the development of improved citizen services taking into consideration integration of agent-based modelling and simulation experience into conceiving, design and implementation activities with a strong focus on technology enabled service systems. Service design is formalized here towards the integration of customer experience, validated through service interaction modelling. Integration of user experience at design stage in the value co-creation process is a possible immediate evolution direction of projects in the Smarter Cities perspective. Guidelines for integrating a modelling and simulation perspective in service design are presented along with the Socio-Technical Systems Engineering process. The case study presented here is dedicated to Smart Transport. The chapter opens a larger discussion on specific research directions and knowledge transfer related to Smart Transport as highlighted in EU projects.

Abstract
Knowledge of the atmospheric turbulence in the telescope line-of-sight is crucial for widefield observations assisted by adaptive optics (AO), particularly tomodel how the point spread function (PSF) elongates across the field of view(FOV) owing to the anisoplanatism effect. The extraction of key astronomical parameters accounts on an accurate representation of the PSF, which call for an accurate anisoplanatism characterisation . This one is, however, a function of the Cn2(h) profile, which is not directly accessible from single-conjugate AO telemetry. It is possible to rely on external profilers, but recent studies have highlighted discrepancies of more than 10 per cent with AO internal measurements, while we aim at better than 1 per cent accuracy for PSF modelling. In order to tackle this limitation, we present focal-plane profiling (FPP) as a Cn2(h) profiling method that relies on post-AO focal-plane images.We demonstrate that such an approach complies with a 1 per cent level of accuracy on the Cn2(h) estimation and establish how this accuracy varies regarding the calibration star magnitudes and their positions in the field. We highlight the fact that photometry and astrometry errors caused by PSF mis-modelling reach respectively 1 per cent and 50 µas using FPP on a Keck baseline, with a preliminary calibration using a star of magnitude H = 14 at 20 arcsec. We validate this concept using Canada's NRC-Herzberg HeNOS testbed images by comparing FPP retrieval with alternative Cn2 (h) measurements on HeNOS. The FPP approach allows the Cn2(h) to be profiled using the SCAO systems and significantly improves the PSF characterization. Such a methodology is also ELT-size-compliant and will be extrapolated to tomographic systems in the near future.

Abstract
Objectives: This article seeks to identify neuroanatomical differences in ADHD through an overview of systematic reviews that report encephalic differences compared to a control group in volume, area, activation likelihood or chemical composition.Methods: We conducted a systematic search using Cochrane guidelines and PRISMA criteria in PubMed, Scopus, Web of Science, Cochrane Database of Systematic Reviews and Database of Abstracts of Reviews of Effects.Results: Results revealed broad encephalic involvement that includes a functional frontal and cingulate hypoactivation and structural differences in corpus callosum, cerebellum and basal nuclei.Conclusions: ADHD symptoms might be due to a multi-network unbalanced functioning hypothesis.

Abstract
Average life expectancy of the humans has been increasing with the advancement of medicine, hence observing an aging of the population in general. Associated with aging, loss of cognitive ability and memory failure are often observed, which can have a great impact on the quality of life and autonomy of people who need to take medication on an hourly basis. In this work, we present the development of an application that helps to manage two key issues for the quality of life of the elderly: the time of medication and the time of medical consultations. The application is managed (information is added, altered or deleted) by a responsible, as a family member or health professional. After analyzing several solutions, it was verified that the biggest problem identified is the difficulty of interaction with the interface (sometimes it presents a lot of information and do not focus on functions of immediate necessity). In this context, we present a solution that focuses on two main objectives: first, to present a clear and simple list of medication and list of information of consultations; and second, to provide a simplified interface for the management of medication by a caregiver (child, nurse, and tutor). In the first development phase, we evaluated the usability of this interface, with ten health care professionals, who evidenced the potentiality of the solution presented here.

Abstract
Short-term load and price forecasting is an important issue in the optimal operation of restructured electric utilities. This paper presents a new intelligent hybrid three-stage model for simultaneous load and price forecasting. The proposed algorithm uses wavelet and Kalman machines for the first stage load and price forecasting. Each of the load and price data is decomposed into different frequency components, and Kalman machine is used to forecast each frequency components of load and price data. Then a Kohonen Self Organizing Map (SOM) finds similar days of load frequency components and feeds them into the second stage forecasting machine. In addition, mutual information based feature selection is used to find the relevant price data and rank them based on their relevance. The second stage uses Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting of load and price frequency components, respectively. The third stage machine uses the second stage outputs and feeds them into its MLP-ANN and ANFIS machines to improve the load and price forecasting accuracy. The proposed three-stage algorithm is applied to Nordpool and mainland Spain power markets. The obtained results are compared with the recent load and price forecast algorithms, and showed that the three-stage algorithm presents a better performance for day-ahead electricity market load and price forecasting.