Abstract
The influence of the massive wall material, thickness and ventilation system on the Trombe wall thermal performance was analysed based on an analytical methodology. Results obtained from experimental work will also be added to this study. During the heating season, for the non-ventilated Trombe wall, the global heat gains decrease is not proportional to the thickness increase, and this ratio depends on the massive wall material heat storage capacity. A ventilation system in the massive wall leads to higher heat gains due to the air convection, but this growth is not in the same proportion for the different materials. If solid brick or earth is used, heat gain values are much higher than those obtained if there is no ventilation system, increasing to the double in the case of earth and 2.5 times more in the case of solid brick. When the massive wall is ventilated and made of granite, an increase in the gains of 44.06% is obtained when compared with the non-ventilated. During the cooling season, closing the ventilation system and the external shutter leads to heat gains considerably lower than those obtained during the heating season. In this case, earth can be a suitable material.

Abstract
Cryptocurrency and blockchain technologies are recently gaining wide adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain's security, asset generation, and maintenance. Although simple and secure, a hash-based PoW like Bitcoin's puzzle is often referred to as "useless", and the used intensive computations are considered "waste" of energy. A myriad of Proof of "something" alternatives have been proposed to mitigate energy consumption; however, they either introduced new security threats and limitations, or the "work" remained far from being really "useful". In this work, we introduce Proof of eXercise (PoX): a sustainable alternative to PoW where an eXercise is a real world matrix-based scientific computation problem. We provide a novel study of the properties of Bitcoin's PoW, the challenges of a more "rational" solution as PoX, and we suggest a comprehensive approach for PoX.

Abstract
Due to the stochastic fluctuant characteristic of solar irradiance, large-scale grid-connected photovoltaic (PV) power plants can bring great difficulties to the operation of the power system. In order to fulfil the sky images based ultra-short term PV power forecasting and enhance the grid consumptive ability of PV power, an accurate model that can map sky images to corresponding surface solar irradiance is very significant. Therefore, in this paper a neural network based irradiance mapping model of solar PV power forecasting using sky image is proposed. First, we combine the theoretical calculation of extraterrestrial solar irradiance and atmospheric optical thickness to establish the clearance surface irradiance model. Second, the sky images observed by total sky imager are processed to extract image features related to solar irradiance. Third, a neural network based irradiance mapping model is built and trained using historical sky images and solar irradiance data. Simulation results show that the proposed model can map sky image features to surface solar irradiance accurately in different weather conditions. © 2018 IEEE

Abstract

Abstract

Abstract
In this paper, deep convolutional neural networks are used to segment heart sounds into their main components. The proposed method is based on the adoption of a novel deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. A further post-processing step is applied to the output of the proposed neural network, which induces the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). The proposed approach is tested on heart sound signals longer than 5 seconds from the publicly available PhysioNet dataset, and it is shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.4% and an average positive predictive value of 94.5% in detecting S1 and S2 sounds.