Abstract
We present MixMash, an interactive tool to assist users in the creation of music mashups based on cross-modal associations between musical content analysis and information visualisation. Our point of departure is a harmonic mixing method for musical mashups by Bernardes et al. [1]. To surpass design limitations identified in the previous method, we propose a new interactive visualisation of multidimensional musical attributes-hierarchical harmonic compatibility, onset density, spectral region, and timbral similarity-extracted from a large collection of audio tracks. All tracks are represented as nodes whose distances and edge connections indicate their harmonic compatibility as a result of a force-directed graph. In addition, we provide a visual language that aims to enhance the tool usability and foster creative endeavour in the search for meaningful music mixes.

Abstract
This paper presents a new state estimation (SE) method including equality constraints to model voltage dependent loads and zero injections. Formulation of conventional SE, assuming simple constant power model for the system's loads, is modified to incorporate voltage dependent load models. Assuming reliable load models and zero injections, it is analytically proved that modeling the equality constraints leads to better SE accuracy. To numerically validate the analytical findings, the proposed SE method is implemented on the IEEE 118-bus test system and the large-scale real-world Iran's power system, and its obtained results are compared with the results of conventional SE. Also, it is shown that considering voltage dependent load model in SE formulation leads to better performance of bad data detection. Moreover, it is illustrated that the accuracy of the proposed SE has low sensitivity to load model identification error.

Abstract
Serious games is a field of research that has evolved substantially with valuable contributions to many application domains and areas. Patients often consider traditional rehabilitation approaches to be repetitive and boring, making it difficult for them to maintain their ongoing interest and to assure the completion of the treatment program. This chapter reviews serious games and the natural and multimodal user interfaces for the health rehabilitation domain. Specifically, it details a framework for the development of serious games that integrates a rich set of features that can be used to improve the designed games with direct benefits to the rehabilitation process. Highlighted features include natural and multimodal interaction, social skills (collaboration and competitiveness), and progress monitoring. Due to the rich set of features supported by the framework, the games' rehabilitation efficacy can be enhanced primarily from an increase in the patient's motivation when exercising the rehabilitation tasks. A preliminary test of the framework with elderly users is described. © 2018, IGI Global.

Abstract
Lisbon is one of the European cities where NO2 and PK10 legal limit values are still exceeded, leading to an Air Quality Plan applicable up to 2020. The developed work combined a detailed emission inventory, monitoring data, and modelling in order to assess if the proposed emission abatement scenarios, focused on the road transport sector, were able to tackle exceedances. A maximum decrease of 14% for PM10 concentrations was achieved, and of 21% for NO2, providing compliance. PM10 smallest reduction is related with higher weight of regional background sources, while for NO2 local traffic has more influence on concentrations.

Abstract
This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences.

Abstract
Machine learning algorithms have evolved by exchanging simplicity and interpretability for accuracy, which prevents their adoption in critical tasks such as healthcare. Progress can be made by improving interpretability of complex models while preserving performance. This work introduces an extension of interpretable mimic learning which teaches in-terpretable models to mimic predictions of complex deep neural networks, not only on binary problems but also in ordinal settings. The results show that the mimic models have comparative performance to Deep Neural Network models, with the advantage of being interpretable.