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Nowadays, the global energy system is in a transition phase, in which the integration of renewable energy is among the main requirements for attenuating climate change. Wind power is a major alternative to supply clean energy; hence, its widespread penetration is being pursued in all end-use sectors. In particular, it is currently noteworthy to analyze the feasibility of deploying small-scale wind power technology to provide cleaner and cheaper energy in the residential sector. As a first step, a technical assessment must be carried out to provide crucial information to intensive energy consumers, providers of small-scale wind power technology, electric energy distribution utilities, and any other party, to help them decide whether or not to deploy small-scale wind turbines. With this aim, we propose to perform such an analysis using a suitable probabilistic paradigm to solve complex decision-making problems with uncertainty, namely Bayesian Intelligence, since wind resources and energy demands are intermittent variables, properly characterized by probability distribution functions. Then, the problem of determining the technical feasibility can be formulated as an investigation into whether or not small-scale wind turbine technology can produce enough energy to cover the excess demand of intensive energy residential consumers to get off high-priced tariffs. For this purpose, we introduce a novel model based on probabilistic reasoning to assess the suitability of small-scale wind turbine technology to produce the said energy, taking into consideration the availability of wind resources and the energy pricing structure. To demonstrate the usefulness and performance of the proposed model, we consider a case study of deploying 5 and 10 kW wind turbines and analyze the feasibility of their implementation in Mexico, where the energy pricing structure and scattered wind resource availability pose difficult challenges., Competing Interests: The authors have declared that no competing interests exist.

Raman scattering plays a key role in unraveling the quantum dynamics of graphene, perhaps the most promising material of recent times. It is crucial to correctly interpret the meaning of the spectra. It is therefore very surprising that the widely accepted understanding of Raman scattering, i.e., Kramers-Heisenberg-Dirac theory, has never been applied to graphene. Doing so here, a remarkable mechanism we term"transition sliding" is uncovered, explaining the uncommon brightness of overtones in graphene. Graphene's dispersive and fixed Raman bands, missing bands, defect density and laser frequency dependence of band intensities, widths of overtone bands, Stokes, anti-Stokes anomalies, and other known properties emerge simply and directly.