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.

Abstract
Regular monitoring is essential to understand coastal morphodynamics and anthropic as well as natural impacts, at different temporal and spatial scales. A stereoscopic video-based terrestrial mobile mapping system, three airborne digital photography systems (mounted on a small manned airplane, a fixed-wing UAV and a multi-rotor UAV, respectively) and airborne LiDAR were compared in terms of: system features, such as range, autonomy, acquisition and operating costs; information supplied, its type and precision; and constraints to system applicability in coastal topographic surveys. Systems differed in resolution, efficiency, and applicability. The terrestrial and UAV-based systems provided the most accurate 3D data, being particularly suited for small-scale, high-resolution surveys. UAVs were easy to deploy, but limited by weather condition, particularly wind speed. Observations from a plane were most efficient and suited for larger areas. Airborne systems had the advantage of being less (UAV) to non-invasive (plane) and thus suitable for the monitoring of sensitive areas (e.g. dunes) and/or areas with difficult access. Systems should be chosen according to the specific survey aims, spatial scale, and local conditions, taking into account their applicability and cost-benefit ratios. They may complement each other to provide a comprehensive picture of coastal morphology and dynamics at different scales.