Probing the scalar-pseudoscalar mixing in the 125 GeV Higgs particle with current data

LHC has found hints for a Higgs particle of 125 GeV. We investigate the possibility that such a particle is a mixture of scalar and pseudoscalar states. For definiteness, we concentrate on a two-Higgs doublet model with explicit CP violation and soft Z(2) violation. Including all Higgs production mechanisms, we determine the current constraints obtained by comparing h -> yy with h -> VV*, and comment on the information which can be gained by measurements of h -> b (b) over bar. We find bounds vertical bar s(2)vertical bar less than or similar to 0.83 at one sigma, where vertical bar s(2)vertical bar = 0 (vertical bar s(2)vertical bar = 1) corresponds to a pure scalar (pure pseudoscalar) state.

Spontaneous leptonic CP violation and nonzero theta(13)

We consider a simple extension of the Standard Model by adding two Higgs triplets and a complex scalar singlet to its particle content. In this framework, the CP symmetry is spontaneously broken at high energies by the complex vacuum expectation value of the scalar singlet. Such a breaking leads to leptonic CP violation at low energies. The model also exhibits an A(4) X Z(4) flavor symmetry which, after being spontaneously broken at a high-energy scale, yields a tribimaximal pattern in the lepton sector. We consider small perturbations around the tribimaximal vacuum alignment condition in order to generate nonzero values of theta(13), as required by the latest neutrino oscillation data. It is shown that the value of theta(13) recently measured by the Daya Bay Reactor Neutrino Experiment can be accommodated in our framework together with large Dirac-type CP violation. We also address the viability of leptogenesis in our model through the out-of-equilibrium decays of the Higgs triplets. In particular, the CP asymmetries in the triplet decays into two leptons are computed and it is shown that the effective leptogenesis and low-energy CP-violating phases are directly linked.

Organization knowledge management change from a complex adaptive systems perpective with ability for ambidexeterity

We are working on the confluence of knowledge management, organizational memory and emergent knowledge with the lens of complex adaptive systems. In order to be fundamentally sustainable organizations search for an adaptive need for managing ambidexterity of day-to-day work and innovation. An organization is an entity of a systemic nature, composed of groups of people who interact to achieve common objectives, making it necessary to capture, store and share interactions knowledge with the organization, this knowledge can be generated in intra-organizational or inter-organizational level. The organizations have organizational memory of knowledge of supported on the Information technology and systems. Each organization, especially in times of uncertainty and radical changes, to meet the demands of the environment, needs timely and sized knowledge on the basis of tacit and explicit. This sizing is a learning process resulting from the interaction that emerges from the relationship between the tacit and explicit knowledge and which we are framing within an approach of Complex Adaptive Systems. The use of complex adaptive systems for building the emerging interdependent relationship, will produce emergent knowledge that will improve the organization unique developing.

Complex fluids at complex surfaces simply complicated

We study wetting and filling of patterned surfaces by a nematic liquid crystal. We focus on three important classes of periodic surfaces: triangular, sinusoidal and rectangular. The results highlight the similarities and differences of nematic wetting of these surfaces and wetting by simple fluids. The interplay of geometry, surface and elastic energies can lead to the suppression of either filling or wetting. The periodic rectangular surface displays re-entrant transitions, with a sequence dry-filled-wet-filled, in the relevant region of parameter space.

Re-entrant phase behaviour of network fluids: A patchy particle model with temperature-dependent valence

We study a model consisting of particles with dissimilar bonding sites ("patches"), which exhibits self-assembly into chains connected by Y-junctions, and investigate its phase behaviour by both simulations and theory. We show that, as the energy cost epsilon(j) of forming Y-junctions increases, the extent of the liquid-vapour coexistence region at lower temperatures and densities is reduced. The phase diagram thus acquires a characteristic "pinched" shape in which the liquid branch density decreases as the temperature is lowered. To our knowledge, this is the first model in which the predicted topological phase transition between a fluid composed of short chains and a fluid rich in Y-junctions is actually observed. Above a certain threshold for epsilon(j), condensation ceases to exist because the entropy gain of forming Y-junctions can no longer offset their energy cost. We also show that the properties of these phase diagrams can be understood in terms of a temperature-dependent effective valence of the patchy particles. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3605703]

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

Complex market integration in MASCEM electricity market simulator

The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players’ interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types.

Exposure to dust in poultry: the importance of task differences for detailed exposure assessment

Dust is a complex mixture of particles of organic and inorganic origin and different gases absorbed in aerosol droplets. In a poultry unit include dried faecal matter and urine, skin flakes, ammonia, carbon dioxide, pollens, feed and litter particles, feathers, grain mites, fungi spores, bacteria, viruses and their constituents. Dust particles vary in size and differentiation between particle size fractions is important in health studies in order to quantify penetration within the respiratory system. A descriptive study was developed in order to assess exposure to particles in a poultry unit during different operations, namely routine examination and floor turn over. Direct-reading equipment was used (Lighthouse, model 3016 IAQ). Particle measurement was performed in 5 different sizes (PM0.5; PM1.0; PM2.5; PM5.0; PM10). The chemical composition of poultry litter was also determined by neutron activation analysis. Normally, the litter of poultry pavilions is turned over weekly and it was during this operation that the higher exposure of particles was observed. In all the tasks considered PM5.0 and PM10.0 were the sizes with higher concentrations values. PM10 is what turns out to have higher values and PM0.5 the lowest values. The chemical element with the highest concentration was Mg (5.7E6 mg.kg-1), followed by K (1.5E4 mg.kg-1), Ca (4.8E3 mg.kg-1), Na (1.7E3 mg.kg-1), Fe (2.1E2 mg.kg-1) and Zn (4.2E1 mg.kg-1). This high presence of particles in the respirable range (<5–7μm) means that poultry dust particles can penetrate into the gas exchange region of the lung. Larger particles (PM10) present a range of concentrations from 5.3E5 and 3.0E6 mg/m3.

A Hexanuclear Mixed-Valence Oxovanadium(IV,V) Complex as a Highly Efficient Alkane Oxidation Catalyst

The new hexanuclear mixed-valence vanadium complex [V3O3(OEt)(ashz)(2)(mu-OEt)](2) (1) with an N,O-donor ligand is reported. It acts as a highly efficient catalyst toward alkane oxidations by aqueous H2O2. Remarkably, high turnover numbers up to 25000 with product yields of up to 27% (based on alkane) stand for one of the most active systems for such reactions.

Copper(II) aza-bis(oxazoline) complex immobilized onto ITQ-2 and MCM-22 based materials as heterogeneous catalysts for the cyclopropanation of styrene

A copper(II) chiral aza-bis(oxazoline) homogeneous catalyst (CuazaBox) was anchored onto the external surface of MCM-22 and ITQ-2 structures, as well as encapsulated into hierarchical MCM-22. The transition metal complex loading onto the porous solids was determined by ICP-AES and the materials were also characterized by elemental analysis (C, N, H, S), FTIR, XPS, TG and low temperature N-2 adsorption isotherms. The materials were tested as heterogeneous catalysts in the benchmark reaction of cyclopropanation of styrene to check the effect of the immobilization procedure on the catalytic parameters, as well as on their reutilization in several catalytic cycles. Catalyst CuazaBox anchored onto the external surface of MCM-22 and ITQ-2 materials were more active and enantioselective in the cyclopropanation of styrene than the corresponding homogeneous phase reaction run under similar experimental conditions. This is due to the propylation of the acidic aza-Box nitrogen. HMCM-22 was nevertheless the best heterogeneous catalyst. Encapsulation of CuazaBox on post-synthesis modified MCM-22 materials led to low activities and enantioselectivities. But reversal on the stereochemical course of the reaction was observed, probably due to confinement effect. (C) 2013 Elsevier Inc. All rights reserved.

Efficient cyclohexane oxidation with hydrogen peroxide catalysed by a C-scorpionate iron(II) complex immobilized on desilicated MOR zeolite

The hydrotris(pyrazol-1-yl)methane iron(II) complex [FeCl2{eta(3)-HC(pz)(3)}] (Fe, pz = pyrazol-1-yl) immobilized on commercial (MOR) or desilicated (MOR-D) zeolite, catalyses the oxidation of cyclohexane with hydrogen peroxide to cyclohexanol and cyclohexanone, under mild conditions. MOR-D/Fe (desilicated zeolite supported [FeCl2{eta(3)-HC(pz)(3)}] complex) provides an outstanding catalytic activity (TON up to 2.90 x 10(3)) with the concomitant overall yield of 38%, and can be easy recovered and reused. The MOR or MOR-D supported hydrotris(pyrazol-1-yl)methane iron(II) complex (MOR/Fe and MOR-D/Fe, respectively) was characterized by X-ray powder diffraction, ICP-AES, and TEM studies as well as by IR spectroscopy and N-2 adsorption at -196 degrees C. The catalytic operational conditions (e.g., reaction time, type and amount of oxidant, presence of acid and type of solvent) were optimized. (C) 2013 Elsevier B.V. All rights reserved.