Abstract
Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.

Abstract
Significant wind power ramps have a remarkable influence on the integration of wind power. Their variability and uncertainty affect to the wind power forecast increasing the error and reducing the reliability in the continued operation of the power system. Ramp events are considered the main source of forecasting error and their study is imperative for an improvement of prediction tools. In this aspect, the first steps to achieve a study of the influence are identifying, grouping and temporal characterizing of the ramp events. This paper develops a methodology for wind power ramp events recognition in order to analyze the relationship between these events and the accuracy of the wind power forecast system according with two criteria: maximum forecast deviation and mean magnitude error. The methodology is validated using real data from the highly aggregated Spanish power system and short time timescale forecasting values. © 2016 IEEE.

Abstract

Abstract
Multi-stage manufacturing, typical in important industrial sectors, is inherently a complex process. The application of the zero defect manufacturing (ZDM) philosophy, together with recent technological advances in cyber-physical systems (CPS), presents significant challenges and opportunities for the implementation of new methodologies towards the continuous system improvement. This paper introduces the main principles of a multi-agent CPS aiming the application of ZDM in multi-stage production systems, which is being developed under the EU H2020 GO0D MAN project. In particular, this paper describes the MAS architecture that allows the distributed data collection and the balancing of the data analysis for monitoring and adaptation among cloud and edge layers, to enable the earlier detection of process and product variability, and the generation of new optimized knowledge by correlating the aggregated data. © 2018, Springer International Publishing AG.

Abstract
ABSTRACTMost human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. While the role of promoters as driver elements in cancer has been recognized, the contribution of alternative promoters to regulation of the cancer transcriptome remains largely unexplored. Here we infer active promoters using RNA-Seq data from 1,188 cancer samples with matched whole genome sequencing data. We find that alternative promoters are a major contributor to context-specific regulation of isoform expression and that alternative promoters are frequently deregulated in cancer, affecting known cancer-genes and novel candidates. Our study suggests that a highly dynamic landscape of active promoters shapes the cancer transcriptome, opening many opportunities to further explore the interplay of regulatory mechanism and noncoding somatic mutations with transcriptional aberrations in cancer.

Abstract
Proliferation of wind power generation is increasingly making this power source an important asset in designs of energy and reserve markets. Intuitively, wind power producers will require the development of new offering strategies that maximize the expected profit in both energy and reserve markets while fulfilling the market rules and its operational limits. In this paper, we implement and exploit the controllability of the proportional control strategy. This strategy allows the splitting of potentially available wind power generation in energy and reserve markets. In addition, we take advantage of better forecast information from the different day-ahead and balancing stages, allowing different shares of energy and reserve in both stages. Under these assumptions, different mathematical methods able to deal with the uncertain nature of wind power generation, namely, stochastic programming, with McCormick relaxation and piecewise linear decision rules are adapted and tested aiming to maximize the expected revenue for participating in both energy and reserve markets, while accounting for estimated balancing costs for failing to provide energy and reserve. A set of numerical examples, as well as a case study based on real data, allow the analysis and evaluation of the performance and behavior of such techniques. An important conclusion is that the use of the proposed approaches offers a degree of freedom in terms of minimizing balancing costs for the wind power producer strategically to participate in both energy and reserve markets. Copyright (c) 2017 John Wiley & Sons, Ltd.