Abstract
It is commonly assumed by the projects demonstrating concepts for positive energy districts in cities across Europe that citizens want and need to be involved in the development of these concepts as an essential condition for positive energy districts to be deployed successfully and to achieve the expected societal goals. Six different research and innovation projects are investigating the different forms of energy citizenship in positive energy districts and their impacts. They aim to apply a transdisciplinary approach to collaborative research and to impact assessment. The interim results are described, and preliminary conclusions on impact are drawn. The projects each used different approaches to engaging citizens, while differentiating between different groups. Progress is monitored but only fragmentary evidence on the impact has been gathered. Transdisciplinary approaches are being developed but are still immature.

Abstract
Social media platforms are used to discuss current events with very complex narratives that become difficult to understand. In this work, we introduce Tweet2Story, a web app to automatically extract narratives from small texts such as tweets and describe them through annotations. By doing this, we aim to mitigate the difficulties existing on creating narratives and give a step towards deeply understanding the actors and their corresponding relations found in a text. We build the web app to be modular and easy-to-use, which allows it to easily incorporate new techniques as they keep getting developed.

Abstract
This paper presents a technical overview for Switched Reluctance Generators (SRG) in Wind Energy Conversion System (WECS) applications. Several topics are discussed, such as the main structures and topologies for SRG converters in WECS, and the optimization control methods to improve the operational efficiency of SRGs in wind power generation systems. A comprehensive overview including the main characteristics of each SRG converter topology and control techniques were discussed. The analysis presented can also serve as a foundation for more advanced versions of SRG control techniques, providing a necessary basis to spur more and, above all, motivate the younger researchers to study magnetless electric machines, and pave the way for higher growth of wind generators based on SRGs.

Abstract

Abstract
AbstractWe advance Mixmash-AIS, a multimodal optimization music mashup creation model for loop recombination at scale. Our motivation is to (1) tackle current scalability limitations in state-of-the-art (brute force) computational mashup models while enforcing the (2) compatibility of audio loops and (3) a pool of diverse mashups that can accommodate user preferences. To this end, we adopt the artificial immune system (AIS) opt-aiNet algorithm to efficiently compute a population of compatible and diverse music mashups from loop recombinations. Optimal mashups result from local minima in a feature space representing harmonic, rhythmic, and spectral musical audio compatibility. We objectively assess the compatibility, diversity, and computational performance of Mixmash-AIS generated mashups compared to a standard genetic algorithm (GA) and a brute force (BF) approach. Furthermore, we conducted a perceptual test to validate the objective evaluation function within Mixmash-AIS in capturing user enjoyment of the computer-generated loop mashups. Our results show that while the GA stands as the most efficient algorithm, the AIS opt-aiNet outperforms both the GA and BF approaches in terms of compatibility and diversity. Our listening test has shown that Mixmash-AIS objective evaluation function significantly captures the perceptual compatibility of loop mashups (p < .001).

Abstract
The Multiple Traveling Salesman Problem (mTSP) is an interesting combinatorial optimization problem due to its numerous real-life applications. It is a problem where m salesmen visit a set of n cities so that each city is visited once. The primary purpose is to minimize the total distance traveled by all salesmen. This paper presents a hybrid approach called GABC-LS that combines an evolutionary algorithm with the swarm intelligence optimization ideas and a local search method. The proposed approach was tested on three instances and produced some better results than the best-known solutions reported in the literature.