Abstract
Home Health Care (HHC) services are growing worldwide and, usually, the home care visits are manually planned, being a time and effort consuming task that leads to a non optimized solution. The use of some optimization techniques can significantly improve the quality of the scheduling solutions, but lacks the achievement of solutions that face the fast reaction to condition changes. In such stochastic and very volatile environments, the fast re-scheduling is crucial to maintain the system in operation. Taking advantage of the inherent distributed and intelligent characteristics of Multi-agent Systems (MAS), this paper introduces a methodology that combines the optimization features provided by centralized scheduling algorithms, e.g. genetic algorithms, with the responsiveness features provided by MAS solutions. The proposed approach was codified in Matlab and NetLogo and applied to a real-world HHC case study. The experimental results showed a significant improvement in the quality of scheduling solutions, as well as in the responsiveness to achieve those solutions. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
There is a set of important selection criteria in the design of fiber optic sensors that determine the compromise between design complexity and performance. Optical fiber sensors not only withstand high temperatures, but they can also operate in different chemical and aqueous media allowing measurements in areas not otherwise accessible. A Fabry-Perot cavity based on an air bubble created in a multimode fiber section is proposed. The air bubble is formed using only cleaving and fusion splicing techniques. The parameters used to produce the microcavities were found empirically. Two different configurations are explored: an inline cavity formed between two sections of MMF, and a fiber tip sensor. In the last, after the air bubble is created, a cleave is made near the cavity, after which the sensor is subjected to several electrical arcs to reshape the cavity and obtain a thin diaphragm. The inline sensor, with a length of similar to 297 mu m, was used to measure strain and presented a sensitivity of 6.48 pm/mu epsilon. Regarding the fiber tip sensor, it was subjected to glycerin/water mixture variations, by immerging the sensing head in several solutions with different concentrations of water in glycerin. In this case, the sensor had a length of similar to 167 mu m and a diaphragm thickness of similar to 20 mu m. As expected, with the increase of the external medium refractive index, the sensor visibility decreased. Furthermore, a wavelength shift towards red was observed, with a sensitivity of 7.81 pm/%wt. Both devices exhibited low dependence to temperature (<1.8 pm/degrees C).

Abstract
Estimation of strain in tendons for tendinopathy assessment is a hot topic within the sports medicine community. It is believed that, if accurately estimated, existing treatment and rehabilitation protocols can be improved and presymptomatic abnormalities can be detected earlier. State-of-the-art studies present inaccurate and highly variable strain estimates, leaving this problem without solution. Out-of-plane motion, present when acquiring two-dimensional (2D) ultrasound (US) images, is a known problem and may be responsible for such errors. This work investigates the benefit of high-frequency, three-dimensional (3D) US imaging to reduce errors in tendon strain estimation. Volumetric US images were acquired in silico, in vitro, and ex vivo using an innovative acquisition approach that combines the acquisition of 2D high-frequency US images with a mechanical guided system. An affine image registration method was used to estimate global strain. 3D strain estimates were then compared with ground-truth values and with 2D strain estimates. The obtained results for in silico data showed a mean absolute error (MAE) of 0.07%, 0.05%, and 0.27% for 3D estimates along axial, lateral direction, and elevation direction and a respective MAE of 0.21% and 0.29% for 2D strain estimates. Although 3D could outperform 2D, this does not occur in in vitro and ex vivo settings, likely due to 3D acquisition artifacts. Comparison against the state-of-the-art methods showed competitive results. The proposed work shows that 3D strain estimates are more accurate than 2D estimates but acquisition of appropriate 3D US images remains a challenge.

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