Several studies have been conducted on Hybrid Power Generation Systems (HPGS) to reduce the existing gap in access to electricity, especially in isolated and difficult access areas. The appropriate use of HPGS allows to power a community, reduces environmental pollution, and improves the performance of a system that only uses Fossil Fuel Generators (FFG). However, despite the efforts made in several countries, there are still isolated areas that remain without electricity due to several factors, such as the high cost of generation, transmission, and energy distribution. For this reason, this work seeks to generate new viable solutions to improve access to electricity especially in isolated areas of Ecuador, although the tool can be applied to any community without constant electricity access. For this purpose, the modeling of an HPGS is proposed, such system is composed of an FFG, a photovoltaic generator, and a hybrid energy storage system (i.e., electrical and thermal). A control algorithm for the FFG is used to adapt the motor rotational speed by using a minimum fuel consumption to supply the load profile. The results are obtained from simulations with real data of three case studies. A reduction in fuel consumption, greenhouse gas emissions, and loss power from renewable energy is demonstrated.

WhatsApp, as the world's most popular messaging application, offers significant opportunities for improving the reach and effectiveness of engagement projects. In collaboration with the International Federation of Red Cross and Red Crescent Societies (IFRC) we designed WhatFutures, a collaborative future forecasting engagement for global youth using WhatsApp. WhatFutures was successfully deployed with 487 players across 5 countries (Kenya, Bulgaria, Finland, Australia and Hong Kong) to inform strategic change within the IFRC. Based on our analysis of the activity - including 16,100 messages, 95 multimedia artifacts, and a post-engagement survey - we present a reflection upon the design decisions underpinning WhatFutures and identify how decisions made around group structures, processes and externalization of outputs influenced engagement and data quality. We conclude with the wider implications of our findings for the design of engagements that best utilize the affordances of existing messaging applications.

A hybrid neural network-first principles modelling scheme is used in this paper, to model an induction motor and to develop a fault detection and isolation (FDI) scheme. The hybrid model combines a partial first principles model, which incorporates the available prior knowledge about the process being modelled, with a neural network which serves as an estimator of unmeasured and unknown process parameters that are difficult to model from first principles. A fault detection and isolation scheme has been defined based on this hybrid model. This suitable model enables system faults to be simulated and the change in corresponding parameters to be predicted without physical experimentation. The detection scheme is based on the calculus of the residues as the difference between the real system and the hybrid model. The isolation scheme is based on neural networks. A three-phase induction motor was simulated under normal operation conditions using the hybrid methodology. Faults in some internal parameters and voltage imbalance between phases supply have been simulated and detected with the FDI scheme, with quite good results.