Abstract
This paper reports on results for the well-known resource-constrained project scheduling problem. A branch-and-bound procedure is developed that takes into account all best performing components from literature, varying branching schemes and search strategies, using the best performing dominance rules and assembling these components into a unified search algorithm. A composite lower bound strategy that statically and dynamically selects the best performing bounds from literature is used to find optimal solutions within reasonable times. An extensive computational experiment is set up to determine the best combination of the various components used in the procedure, in order to benchmark the current existing knowledge on four different datasets from the literature. By varying the network topology, resource scarceness and the size of the projects, the computational experiments are carried out on a diverse set of projects. The procedure was able to find some new lower bounds and optimal solutions for the PSPLIB instances. Moreover, new best known results are reported for other, more diverse datasets that can be used in future research studies. The experiments revealed that even project instances with 30 activities cannot be solved to optimality when the topological structure is varied.

Abstract

Abstract
The motion of cloud over photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power, so the tracking of cloud motion is very crucial. In this study, Block-matching algorithm, Optical Flow algorithm and feature matching algorithm are three prevailing methods. However, as a rigid registration method, Block-matching cannot obtain the parameters of cloud deformation or rotation. The accuracy of the optical flow, which is based on the assumption that the image grayscale is not changed, is easily disturbed by noise. When the image texture information is not rich enough, the accuracy of the feature matching will also be reduced. That is, in order to improve their robustness, they must be combined through a certain strategy. Therefore, a pattern classification and PSO optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed in this paper. The method consists of two parts. Firstly, we use k-means clustering method and texture features based on Gray-Level Co-occurrence Matrix (GLCM) to classify the clouds. Because texture can adequately reflect image information, compared with other image features, it can better take into account both the macro nature and the fine structure of images. Secondly, for different cloud classes, we build the corresponding combined calculation modeling to obtain cloud motion speed. The Particle Swarm Optimization algorithm is used to give different weights to different methods to adapt to different clouds. The performances of the method are investigated using real data recorded at Yunnan Electric Power Research Institute. Under the measurement of common precision index, the comparisons with various benchmark methods show the effectiveness of the proposed approaches over cloud tracing. © 2018 IEEE

Abstract
The problem of information overload motivated the appearance of Recommender Systems. From the several open problems in this area, the decision of which is the best recommendation algorithm for a specific problem is one of the most important and less studied. The current trend to solve this problem is the experimental evaluation of several recommendation algorithms in a handful of datasets. However, these studies require an extensive amount of computational resources, particularly processing time. To avoid these drawbacks, researchers have investigated the use of Metalearning to select the best recommendation algorithms in different scopes. Such studies allow to understand the relationships between data characteristics and the relative performance of recommendation algorithms, which can be used to select the best algorithm(s) for a new problem. The contributions of this study are two-fold: 1) to identify and discuss the key concepts of algorithm selection for recommendation algorithms via a systematic literature review and 2) to perform an experimental study on the Metalearning approaches reviewed in order to identify the most promising concepts for automatic selection of recommendation algorithms.

Abstract
In Ecuador, as in the world the most commonly used prostheses are only aesthetic, and the problem with the people that uses them is that they don’t feel fully comfortable and independent with their activities, so in looking for solving this problem, researchers have designed different active prostheses but as the technology advances, these equipment gets more complex, heavy and expensive, so the people who need them doesn’t feel acceptance. The goal of this and the further investigations is development of a new design that can properly integrate the top technologies in a skeletal design which makes natural movements and will improve the quality of life of the people who uses it. This paper analyses the different designs on the available prosthesis and extract from them the best characteristics of the upper limb prostheses design.

Abstract
Atmospheric turbulence and precise measurement of the astrometric baseline vector between any two telescopes are two major challenges in implementing phase-referenced interferometric astrometry and imaging. They limit the performance of a fibre-fed interferometer by degrading the instrument sensitivity and the precision of astrometric measurements and by introducing image reconstruction errors due to inaccurate phases. A multiple-beam acquisition and guiding camera was built to meet these challenges for a recently commissioned four-beam combiner instrument, GRAVITY, at the European Southern Observatory Very Large Telescope Interferometer. For each telescope beam, it measures (a) field tip-tilts by imaging stars in the sky, (b) telescope pupil shifts by imaging pupil reference laser beacons installed on each telescope using a 2x2 lenslet and (c) higher-order aberrations using a 9x9 Shack-Hartmann. The telescope pupils are imaged to provide visual monitoring while observing. These measurements enable active field and pupil guiding by actuating a train of tip-tilt mirrors placed in the pupil and field planes, respectively. The Shack-Hartmann measured quasi-static aberrations are used to focus the auxiliary telescopes and allow the possibility of correcting the non-common path errors between the adaptive optics systems of the unit telescopes and GRAVITY. The guiding stabilizes the light injection into single-mode fibres, increasing sensitivity and reducing the astrometric and image reconstruction errors. The beam guiding enables us to achieve an astrometric error of less than 50 mu as. Here, we report on the data reduction methods and laboratory tests of the multiple-beam acquisition and guiding camera and its performance on-sky.