Abstract
The detection of overlapping communities in complex networks has motivated recent research in relevant fields. Aiming to address this problem, we propose a Markov-dynamics-based algorithm, called UEOC, which means 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge of the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and has been compared with a set of competing algorithms. The experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities.

Abstract
Under the current European energy policy towards a sustainable environment, the optimization of the hydropower resources is of crucial importance. In this paper, a mixed-integer nonlinear programming approach is proposed for the short-term hydro scheduling problem, considering head-dependency and discontinuous operating regions. As new contributions to earlier studies, market uncertainty is introduced via price scenarios and risk management is incorporated by limiting the volatility of the expected profit through the conditional value-at-risk. Besides, plant scheduling and pool offering by the hydro power producer are simultaneously considered to solve a realistic hydro system with three cascaded reservoirs. Finally, conclusions are duly drawn.

Abstract
The integration of cell division in root growth and development requires mediation of developmental and physiological signals through regulation of cyclin-dependent kinase activity. Cells within the pericycle form de novo lateral root meristems, and D-type cyclins (CYCD), as regulators of the G(1)-to-S phase cell cycle transition, are anticipated to play a role. Here, we show that the D-type cyclin protein CYCD2;1 is nuclear in Arabidopsis thaliana root cells, with the highest concentration in apical and lateral meristems. Loss of CYCD2;1 has a marginal effect on unstimulated lateral root density, but CYCD2;1 is rate-limiting for the response to low levels of exogenous auxin. However, while CYCD2;1 expression requires sucrose, it does not respond to auxin. The protein Inhibitor-Interactor of CDK/Kip Related Protein2 (ICK2/KRP2), which interacts with CYCD2;1, inhibits lateral root formation, and ick2/krp2 mutants show increased lateral root density. ICK2/KRP2 can modulate the nuclear levels of CYCD2;1, and since auxin reduces ICK2/KRP2 protein levels, it affects both activity and cellular distribution of CYCD2;1. Hence, as ICK2/KRP2 levels decrease, the increase in lateral root density depends on CYCD2;1, irrespective of ICK2/CYCD2;1 nuclear localization. We propose that ICK2/KRP2 restrains root ramification by maintaining CYCD2;1 inactive and that this modulates pericycle responses to auxin fluctuations.

Abstract
In this paper, we apply the following four power indices to the Portuguese Parliament: Shapley-Shubik index, Banzhaf index, Deegan-Packel index and Public Good Index. We also present the main concepts related with simple games and discuss the features of each power index by means of their axiomatic characterizations.

Abstract
The problem of real-time extraction of meaningful patterns from time-changing data streams is of increasing importance for the machine learning and data mining communities. Regression in time-changing data streams is a relatively unexplored topic, despite the apparent applications. This paper proposes an efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams. The algorithm is evaluated extensively in a variety of settings involving artificial and real data. To the best of our knowledge there is no other general purpose algorithm for incremental learning regression/model trees able to perform explicit change detection and informed adaptation. The algorithm performs online and in real-time, observes each example only once at the speed of arrival, and maintains at any-time a ready-to-use model tree. The tree leaves contain linear models induced online from the examples assigned to them, a process with low complexity. The algorithm has mechanisms for drift detection and model adaptation, which enable it to maintain accurate and updated regression models at any time. The drift detection mechanism exploits the structure of the tree in the process of local change detection. As a response to local drift, the algorithm is able to update the tree structure only locally. This approach improves the any-time performance and greatly reduces the costs of adaptation.

Abstract
A temperature-insensitive strain sensor based on Four-Wave Mixing (FWM) using two Raman fiber Bragg grating (FBG) lasers with cooperative Rayleigh scattering is proposed. Two FBG were used to form two linear cavities laser sensors based on Raman amplification combined with cooperative Rayleigh scattering. Due to the very low dispersion coefficient of the fiber, it is possible to obtain the FWM using the two lasers. This configuration allows the operation as a temperature-insensitive strain sensor where both sensors have the same sensitivity to temperature but only one of the FBG laser is sensitive to strain. The difference between the wavelengths of the signal sensor and the converted signal presents a strain coefficient sensitivity of 2 pm/mu epsilon with insensitivity to temperature. The FWM efficiency is also dependent on the applied strain, but it is temperature independent, presenting a maximum sensibility of 0.01 dB/mu epsilon.