Synthesis, structure, and optical properties of an alternating calix[4]arene-based meta-linked phenylene ethynylene copolymer

Novel alternating copolymers comprising biscalix[4]arene-p-phenylene ethynylene and m-phenylene ethynylene units (CALIX-m-PPE) were synthesized using the Sonogashira-Hagihara cross-coupling polymerization. Good isolated yields (60-80%) were achieved for the polymers that show M-n ranging from 1.4 x 10(4) to 5.1 x 10(4) gmol(-1) (gel permeation chromatography analysis), depending on specific polymerization conditions. The structural analysis of CALIX-m-PPE was performed by H-1, C-13, C-13-H-1 heteronuclear single quantum correlation (HSQC), C-13-H-1 heteronuclear multiple bond correlation (HMBC), correlation spectroscopy (COSY), and nuclear overhauser effect spectroscopy (NOESY) in addition to Fourier transform-Infrared spectroscopy and microanalysis allowing its full characterization. Depending on the reaction setup, variable amounts (16-45%) of diyne units were found in polymers although their photophysical properties are essentially the same. It is demonstrated that CALIX-m-PPE does not form ground-or excited-state interchain interactions owing to the highly crowded environment of the main-chain imparted by both calix[4]arene side units which behave as insulators inhibiting main-chain pi-pi staking. It was also found that the luminescent properties of CALIX-m-PPE are markedly different from those of an all-p-linked phenylene ethynylene copolymer (CALIX-p-PPE) previously reported. The unexpected appearance of a low-energy emission band at 426 nm, in addition to the locally excited-state emission (365 nm), together with a quite low fluorescence quantum yield (Phi = 0.02) and a double-exponential decay dynamics led to the formulation of an intramolecular exciplex as the new emissive species.

Scheduling of head-dependent cascaded reservoirs considering discharge ramping constraints and start/stop of units

This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning head-dependent reservoirs under competitive environment. We propose a novel method, based on mixed-integer nonlinear programming (MINLP), for optimising power generation efficiency. This method considers hydroelectric power generation as a nonlinear function of water discharge and of the head. The main contribution of this paper is that discharge ramping constraints and start/stop of units are also considered, in order to obtain more realistic and feasible results. The proposed method has been applied successfully to solve two case studies based on Portuguese cascaded hydro systems, providing a higher profit at an acceptable computation time in comparison with classical optimisation methods based on mixed-integer linear programming (MILP).

A practical approach for profit-based unit commitment with emission limitations

This paper proposes a practical approach for profit-based unit commitment (PBUC) with emission limitations. Under deregulation, unit commitment has evolved from a minimum-cost optimisation problem to a profit-based optimisation problem. However, as a consequence of growing environmental concern, the impact of fossil-fuelled power plants must be considered, giving rise to emission limitations. The simultaneous address of the profit with the emission is taken into account in our practical approach by a multiobjective optimisation (MO) problem. Hence, trade-off Curves between profit and emission are obtained for different energy price profiles, in a way to aid decision-makers concerning emission allowance trading. Moreover, a new parameter is presented, ratio of change, and the corresponding gradient angle, enabling the proper selection of a compromise commitment for the units. A case study based on the standard IEEE 30-bus system is presented to illustrate the proficiency Of Our practical approach for the new competitive and environmentally constrained electricity supply industry.

Hydro energy systems management in Portugal: Profit-based evaluation of a mixed-integer nonlinear approach

In this paper, a novel mixed-integer nonlinear approach is proposed to solve the short-term hydro scheduling problem in the day-ahead electricity market, considering not only head-dependency, but also start/stop of units, discontinuous operating regions and discharge ramping constraints. Results from a case study based on one of the main Portuguese cascaded hydro energy systems are presented, showing that the proposedmixed-integer nonlinear approach is proficient. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Sensing Cloud Optimization to Solve ED of Units with Valve-Point Effects and Multi-fuels

In this paper a solution to an highly constrained and non-convex economical dispatch (ED) problem with a meta-heuristic technique named Sensing Cloud Optimization (SCO) is presented. The proposed meta-heuristic is based on a cloud of particles whose central point represents the objective function value and the remaining particles act as sensors "to fill" the search space and "guide" the central particle so it moves into the best direction. To demonstrate its performance, a case study with multi-fuel units and valve- point effects is presented.

FMRI 3D registration based on Fourier space subsets using neural networks

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

Renewable power forecast to scheduling of thermal units

In this work is discussed the importance of the renewable production forecast in an island environment. A probabilistic forecast based on kernel density estimators is proposed. The aggregation of these forecasts, allows the determination of thermal generation amount needed to schedule and operating a power grid of an island with high penetration of renewable generation. A case study based on electric system of S. Miguel Island is presented. The results show that the forecast techniques are an imperative tool help the grid management.

Self-scheduling and bidding strategies of thermal units with stochastic emission constraints

This paper is on the self-scheduling problem for a thermal power producer taking part in a pool-based electricity market as a price-taker, having bilateral contracts and emission-constrained. An approach based on stochastic mixed-integer linear programming approach is proposed for solving the self-scheduling problem. Uncertainty regarding electricity price is considered through a set of scenarios computed by simulation and scenario-reduction. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. A requirement on emission allowances to mitigate carbon footprint is modelled by a stochastic constraint. Supply functions for different emission allowance levels are accessed in order to establish the optimal bidding strategy. A case study is presented to illustrate the usefulness and the proficiency of the proposed approach in supporting biding strategies. (C) 2014 Elsevier Ltd. All rights reserved.

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

Vertex component analysis GPU-based implementation for hyperspectral unmixing

Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis ( VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Many Hyperspectral imagery applications require a response in real time or near-real time. Thus, to met this requirement this paper proposes a parallel implementation of VCA developed for graphics processing units. The impact on the complexity and on the accuracy of the proposed parallel implementation of VCA is examined using both simulated and real hyperspectral datasets.

Spatiotemporal control over the co-conformational switching in pH-responsive flavylium-based multistate pseudorotaxanes

A multistate molecular dyad containing flavylium and viologen units was synthesized and the pH dependent thermodynamics of the network completely characterized by a variety of spectroscopic techniques such as NMR, UV-vis and stopped-flow. The flavylium cation is only stable at acidic pH values. Above pH ≈ 5 the hydration of the flavylium leads to the formation of the hemiketal followed by ring-opening tautomerization to give the cis-chalcone. Finally, this last species isomerizes to give the trans-chalcone. For the present system only the flavylium cation and the trans-chalcone species could be detected as being thermodynamically stable. The hemiketal and the cis-chalcone are kinetic intermediates with negligible concentrations at the equilibrium. All stable species of the network were found to form 1 : 1 and 2 : 1 host : guest complexes with cucurbit[7]uril (CB7) with association constants in the ranges 10(5)-10(8) M(-1) and 10(3)-10(4) M(-1), respectively. The 1 : 1 complexes were particularly interesting to devise pH responsive bistable pseudorotaxanes: at basic pH values (≈12) the flavylium cation interconverts into the deprotonated trans-chalcone in a few minutes and under these conditions the CB7 wheel was found to be located around the viologen unit. A decrease in pH to values around 1 regenerates the flavylium cation in seconds and the macrocycle is translocated to the middle of the axle. On the other hand, if the pH is decreased to 6, the deprotonated trans-chalcone is neutralized to give a metastable species that evolves to the thermodynamically stable flavylium cation in ca. 20 hours. By taking advantage of the pH-dependent kinetics of the trans-chalcone/flavylium interconversion, spatiotemporal control of the molecular organization in pseudorotaxane systems can be achieved.