Abstract
Gas-fired units and power-to-gas facilities provide pivotal backups for power systems with volatile renewable generations. The deepened system interdependence calls for elaborate consideration of network models of both natural gas and power systems, as well as uncertain factors. This paper proposes a data-driven distributionally robust optimization model for the optimal gas-power flow problem with uncertain wind generation. The concept of zonal line pack and line pack reserve are raised to topologically distinguish fuel suppliers of gas-fired units and ensure gas system operating security during reserve deployment. Wind power uncertainty is described by an ambiguity set, i.e., a family of candidate distributions around an empirical distribution in the sense of Wasserstein distance. A convex optimization-based solution procedure is developed, which entails solving only second-order cone programs. Computational results validate the effectiveness of the proposed models and methods.

Abstract
This paper proposes a stochastic decision making model for a demand response (DR) aggregator as an interface between the market and customers in a competitive environment. The DR aggregator participates in day-ahead (DA) energy and balancing markets as well as offers selling price to the customers to maximize its expected profit, considering the reaction of customers to the rivals' offering prices. Moreover, the effect of load reduction due to implementing DR contracts on the decision making process of the DR aggregator is assessed. However, the main focus is on the operation of both shiftable and sheddable loads in price-based DR programs with detail. In order to investigate the behavior of different DR actions from the DR aggregator viewpoint, the restrictions imposed by the preferences of customers to the decisions made by the DR aggregators are modeled via a bi-level stochastic programming approach. The upper level represents the decisions made by the DR aggregator, while the lower level models the customers' behavior. To deal with various uncertainties, a risk-constrained scenario-based stochastic programming framework is presented where the DR aggregator's risk aversion is modeled using conditional value at risk (CVaR) method. Finally, a detailed illustrative case study based on the Nordic energy market data is provided and the effects of different DR actions and risk aversion factor on the profit of the aggregator are analyzed.

Abstract
Teaching in the scientific area of Digital Media has been evolving to meet the challenges facing our society, particularly to create technologies that effectively provide added value to the human being, both professionally and in the personal life. This article presents a new teaching methodology that combines "creative programming" and "creatively learning programming", intertwining the use of digital games as the metaphor for the programming constructs, and gamification to enhance engagement and regular work. This is backed up on the knowledge that the introductory learning of programming is a process that requires the acquisition of cognitive skills, which fit into what is defined as Computational Thinking, but also that the process of learning programming has a high level of complexity. This causes specific difficulties that have been observed over several years, mainly as the practice requires specific mathematical skills, not so common in Digital Media students. The teaching methodology is based on a constructionist approach, with PBL, defining learning paths supported on digital games development. The difficulty inherent in constructing a problematic base, coherent with the learning process, led to the development of the Creative Programming Matrix, based on the triadic certification method. This matrix enables the teacher to select, organize and develop the best game examples to support the learning process. This work has been preliminarily evaluated through questionnaires to students and surveys to faculty that teaches programming courses. This method was instantiated in the restructuring of a course on a Master program in Multimedia. A set of gamified activities promoted a natural progression in the learning, essential for a first approach to the programming. From a set of clear objectives, the target behaviours and the profile of the students were defined, the cycles of involvement and the necessary tools were developed, not forgetting that learning should be fun! The results validate a higher efficacy and engagement of the students.

Abstract
Nowadays most people carry a smartphone with built-in sensors (e.g., accelerometers, gyroscopes) capable of providing useful data for Human Activity Recognition (HAR). Machine learning classification methods have been intensively researched and developed for HAR systems, each with different accuracy and performance levels. However, acquiring sensor data and executing machine learning classifiers require computational power and consume energy. As such, a number of factors, such as inadequate preprocessing, can have a negative impact on the overall HAR performance, even on high-end handheld devices. While high accuracy can be extremely important in some applications, the device’s battery life can be highly critical to the end-user. This paper is focused on the k-nearest neighbors’ algorithm (kNN), one of the most used algorithms in HAR systems, and research and develop energy-efficient implementations for mobile devices. We focus on a kNN implementation based on Locality-Sensitive Hashing (LSH) with a significant positive impact on the device’s battery life, fully integrated into a mobile HAR Android application able to classify human activities in real-time. The proposed kNN implementation was able to achieve execution time reductions of 50% over other versions of kNN with average accuracy of 96.55% when considering 8 human activities. © 2019, Springer Nature Switzerland AG.

Abstract
Purpose The purpose of this paper is to examine and compare the influence of age, education, income, product involvement and sales promotion (SP) characteristics on consumer attitudes towards SP across eight culturally dissimilar environments. Design/methodology/approach A multi-country mall intercept and mail survey was conducted in Brunei, China, Hong Kong, Indonesia, Malaysia, New Zealand, Singapore and Thailand (n=4,125 respondents). Findings Country, education level and income significantly influence consumer attitudes towards SP. Some countries show a significant monetary value interaction effect. Consumers using delayed-reward SPT reported a significantly more positive attitude towards SP. Discounts and coupons are the two most highly ranked SP across the sampled countries. Research limitations/implications - Limitations include the use of intercept and mail sampling. Extending the study to include additional Asian countries and other regions would benefit the understanding of cultural influences on SP. Practical implications - Multinational marketing managers should consider three aspects of SP implementation strategy: cultural and demographic factors, interaction between delayed-reward SP and socio-demographics variables; country specific SP preferences to promote both sales and brand equity. Originality/value This study investigates and extends research on SP across cultures. In particular the research helps better understand the impact of demographic factors and culture on attitudes towards SP, and implementation of global promotions.

Abstract
In this paper, we propose an active fault tolerant control (FTC) to regulate the active and reactive output powers of a voltage source converter (VSC) in the case of actuator failure. The active fault tolerant controller of the VSC which connects a distributed energy resource to the distribution power grid is achieved through the fault diagnostic and controller reconfiguration units. The diagnostic unit reveals the actuator failure by comparing the known inputs and measured outputs of VSC with those of the faultless model of the system and testing their consistency. In the case of actuator failure, the reconfiguration unit adapts the controller to the faulty system which enables the VSC to track the desired active and reactive output powers. The reconfiguration unit is designed using the virtual actuator which does not interfere with the regular controller of the VSC. The effectiveness of the proposed active FTC is evaluated by the numerical simulation of a VSC connected to the AC distribution grid.