Energy-Aware Routing And Grooming For IP Transport Over WDM MLR Networks

In this work a mixed integer optimization linear programming (MILP) model was applied to mixed line rate (MLR) IP over WDM and IP over OTN over WDM (with and without OTN grooming) networks, with aim to reduce network energy consumption. Energy-aware and energy-aware & short-path routing techniques were used. Simulations were made based on a real network topology as well as on forecasts of traffic matrix based on statistical data from 2005 up to 2017. Energy aware routing optimization model on IPoWDM network, showed the lowest energy consumption along all years, and once compared with energy-aware & short-path routing, has led to an overall reduction in energy consumption up to 29%, expecting to save even more than shortest-path routing. © 2014 IEEE.

Supply chain optimization of residual forestry biomass for bioenergy production: the case study of Portugal

Within a large set of renewable energies being explored to tackle energy sourcing problems, bioenergy can represent an attractive solution if effectively managed. The supply chain design supported by mathematical programming can be used as a decision support tool to the successful bioenergy production systems establishment. This strategic decision problem is addressed in this paper where we intent to study the design of the residual forestry biomass to bioelectricity production in the Portuguese context. In order to contribute to attain better solutions a mixed integer linear programming (MILP) model is developed and applied in order to optimize the design and planning of the bioenergy supply chain. While minimizing the total supply chain cost the production energy facilities capacity and location are defined. The model also includes the optimal selection of biomass amounts and sources, the transportation modes selection, and links that must be established for biomass transportation and products delivers to markets. Results illustrate the positive contribution of the mathematical programming approach to achieve viable economic solutions. Sensitivity analysis on the most uncertain parameters was performed: biomass availability, transportation costs, fixed operating costs and investment costs. (C) 2015 Elsevier Ltd. All rights reserved.

Self-Scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach

This paper is on the self-scheduling for a power producer taking part in day-ahead joint energy and spinning reserve markets and aiming at a short-term coordination of wind power plants with concentrated solar power plants having thermal energy storage. The short-term coordination is formulated as a mixed-integer linear programming problem given as the maximization of profit subjected to technical operation constraints, including the ones related to a transmission line. Probability density functions are used to model the variability of the hourly wind speed and the solar irradiation in regard to a negative correlation. Case studies based on an Iberian Peninsula wind and concentrated solar power plants are presented, providing the optimal energy and spinning reserve for the short-term self-scheduling in order to unveil the coordination benefits and synergies between wind and solar resources. Results and sensitivity analysis are in favour of the coordination, showing an increase on profit, allowing for spinning reserve, reducing the need for curtailment, increasing the transmission line capacity factor. (C) 2014 Elsevier Ltd. All rights reserved.

The Sticky Information Macro Model: Beyond Perfect Foresight

Sticky information monetary models have been used in the macroeconomic literature to explain some of the observed features regarding inflation dynamics. In this paper, we explore the consequences of relaxing the rational expectations assumption usually taken in this type of model; in particular, by considering expectations formed through adaptive learning, it is possible to arrive to results other than the trivial convergence to a fixed point long-term equilibrium. The results involve the possibility of endogenous cyclical motion (periodic and a-periodic), which emerges essentially in scenarios of hyperinflation. In low inflation settings, the introduction of learning implies a less severe impact of monetary shocks that, nevertheless, tend to last for additional time periods relative to the pure perfect foresight setup.

Stochastic effects in a seasonally forced epidemic model

The interplay of seasonality, the system's nonlinearities and intrinsic stochasticity, is studied for a seasonally forced susceptible-exposed-infective-recovered stochastic model. The model is explored in the parameter region that corresponds to childhood infectious diseases such as measles. The power spectrum of the stochastic fluctuations around the attractors of the deterministic system that describes the model in the thermodynamic limit is computed analytically and validated by stochastic simulations for large system sizes. Size effects are studied through additional simulations. Other effects such as switching between coexisting attractors induced by stochasticity often mentioned in the literature as playing an important role in the dynamics of childhood infectious diseases are also investigated. The main conclusion is that stochastic amplification, rather than these effects, is the key ingredient to understand the observed incidence patterns.

Improving a Cluster Based Directional Channel Model in Realistic Macro-Cell Environment

In this paper a realistic directional channel model that is an extension of the COST 273 channel model is presented. The model uses a cluster of scatterers and visibility region generation based strategy with increased realism, due to the introduction of terrain and clutter information. New approaches for path-loss prediction and line of sight modeling are considered, affecting the cluster path gain model implementation. The new model was implemented using terrain, clutter, street and user mobility information for the city of Lisbon, Portugal. Some of the model's outputs are presented, mainly path loss and small/large-scale fading statistics.

A two-Higgs doublet model with remarkable CP properties

We analyze generalized CP symmetries of two-Higgs doublet models, extending them from the scalar to the fermion sector of the theory. We show that, other than the usual CP transformation, there is only one of those symmetries which does not imply massless charged fermions. That single model which accommodates a fermionic mass spectrum compatible with experimental data possesses a remarkable feature. Through a soft breaking of the symmetry it displays a new type of spontaneous CP violation, which does not occur in the scalar sector responsible for the symmetry breaking mechanism but, rather, in the fermion sector.

Basis invariant conditions for supersymmetry in the two-Higgs-doublet model

The minimal supersymmetric standard model involves a rather restrictive Higgs potential with two Higgs fields. Recently, the full set of classes of symmetries allowed in the most general two-Higgs-doublet model was identified; these classes do not include the supersymmetric limit as a particular class. Thus, a physically meaningful definition of the supersymmetric limit must involve the interaction of the Higgs sector with other sectors of the theory. Here we show how one can construct basis invariant probes of supersymmetry involving both the Higgs sector and the gaugino-Higgsino-Higgs interactions.

MDAI: Model based Design in Automobile Industry

It is proposed a new approach based on a methodology, assisted by a tool, to create new products in the automobile industry based on previous defined processes and experiences inspired on a set of best practices or principles: it is based on high-level models or specifications; it is component-based architecture centric; it is based on generative programming techniques. This approach follows in essence the MDA (Model Driven Architecture) philosophy with some specific characteristics. We propose a repository that keeps related information, such as models, applications, design information, generated artifacts and even information concerning the development process itself (e.g., generation steps, tests and integration milestones). Generically, this methodology receives the users' requirements to a new product (e.g., functional, non-functional, product specification) as its main inputs and produces a set of artifacts (e.g., design parts, process validation output) as its main output, that will be integrated in the engineer design tool (e.g. CAD system) facilitating the work.

Chaos and crises in a model for cooperative hunting: A symbolic dynamics approach

In this work we investigate the population dynamics of cooperative hunting extending the McCann and Yodzis model for a three-species food chain system with a predator, a prey, and a resource species. The new model considers that a given fraction sigma of predators cooperates in prey's hunting, while the rest of the population 1-sigma hunts without cooperation. We use the theory of symbolic dynamics to study the topological entropy and the parameter space ordering of the kneading sequences associated with one-dimensional maps that reproduce significant aspects of the dynamics of the species under several degrees of cooperative hunting. Our model also allows us to investigate the so-called deterministic extinction via chaotic crisis and transient chaos in the framework of cooperative hunting. The symbolic sequences allow us to identify a critical boundary in the parameter spaces (K, C-0) and (K, sigma) which separates two scenarios: (i) all-species coexistence and (ii) predator's extinction via chaotic crisis. We show that the crisis value of the carrying capacity K-c decreases at increasing sigma, indicating that predator's populations with high degree of cooperative hunting are more sensitive to the chaotic crises. We also show that the control method of Dhamala and Lai [Phys. Rev. E 59, 1646 (1999)] can sustain the chaotic behavior after the crisis for systems with cooperative hunting. We finally analyze and quantify the inner structure of the target regions obtained with this control method for wider parameter values beyond the crisis, showing a power law dependence of the extinction transients on such critical parameters.

A 3-phase model for VIS/NIR mu C-Si : H p-i-n detectors

The spectral response and the photocurrent delivered by entirely microcrystalline p-i-n-Si:H detectors an analysed under different applied bias and light illumination conditions. The spectral response and the internal collection depend not only on the energy range but also on the illumination side. Under [p]- and [n]-side irradiation, the internal collection characteristics have an atypical shape. It is high for applied bias and lower than the open circuit voltage, shows a steep decrease near the open circuit voltage (higher under [n]-side illumination) and levels off for higher voltages. Additionally, the numerical modeling of the VIS/NIR detector, based on the band discontinuities near the grain boundaries and interfaces, complements the study and gives insight into the internal physical process.

The US actuarial balance model for the pay-as-you-go system and its application to Spain

The aim of this paper is to formulate an approximation of the US actuarial balance model and apply it to the Spanish public retirement pension system under various scenarios in order to determine a consistent indicator of the system's financial state comparable to those used by the most advanced social security systems. This will enable us to answer the question as to whether there is any justification for reforming the pension system in Spain. This type of actuarial balance uses projections to show future challenges to the financial side of the pension system deriving basically from ageing, the projected increase in longevity and fluctuations in economic activity. If one is compiled periodically it can provide various indicators to help depoliticize the management of the pay-as-you-go system by bringing the planning horizons of politicians and the system itself closer together.

A model to forecast financial failure, in non financial galician SMES

We are concerned with providing more empirical evidence on forecast failure, developing forecast models, and examining the impact of events such as audit reports. A joint consideration of classic financial ratios and relevant external indicators leads us to build a basic prediction model focused in non-financial Galician SMEs. Explanatory variables are relevant financial indicators from the viewpoint of the financial logic and financial failure theory. The paper explores three mathematical models: discriminant analysis, Logit, and linear multivariate regression. We conclude that, even though they both offer high explanatory and predictive abilities, Logit and MDA models should be used and interpreted jointly.

Model risk in the pricing of exotic options

The growth experimented in recent years in both the variety and volume of structured products implies that banks and other financial institutions have become increasingly exposed to model risk. In this article we focus on the model risk associated with the local volatility (LV) model and with the Variance Gamma (VG) model. The results show that the LV model performs better than the VG model in terms of its ability to match the market prices of European options. Nevertheless, both models are subject to significant pricing errors when compared with the stochastic volatility framework.

Volatility forecasting with range models: An evaluation of new alternatives to the CARR model

The aim of this paper is to analyze the forecasting ability of the CARR model proposed by Chou (2005) using the S&P 500. We extend the data sample, allowing for the analysis of different stock market circumstances and propose the use of various range estimators in order to analyze their forecasting performance. Our results show that there are two range-based models that outperform the forecasting ability of the GARCH model. The Parkinson model is better for upward trends and volatilities which are higher and lower than the mean while the CARR model is better for downward trends and mean volatilities.