A cyclic time-dependent Markov process to model daily patterns in wind turbine power production

Wind energy is becoming a top contributor to the renewable energy mix, which raises potential reliability issues for the grid due to the fluctuating nature of its source. To achieve adequate reserve commitment and to promote market participation, it is necessary to provide models that can capture daily patterns in wind power production. This paper presents a cyclic inhomogeneous Markov process, which is based on a three-dimensional state-space (wind power, speed and direction). Each time-dependent transition probability is expressed as a Bernstein polynomial. The model parameters are estimated by solving a constrained optimization problem: The objective function combines two maximum likelihood estimators, one to ensure that the Markov process long-term behavior reproduces the data accurately and another to capture daily fluctuations. A convex formulation for the overall optimization problem is presented and its applicability demonstrated through the analysis of a case-study. The proposed model is capable of reproducing the diurnal patterns of a three-year dataset collected from a wind turbine located in a mountainous region in Portugal. In addition, it is shown how to compute persistence statistics directly from the Markov process transition matrices. Based on the case-study, the power production persistence through the daily cycle is analysed and discussed

Simulations of Antigenic Variability in Influenza A

Computational models of the immune system (IS) and pathogenic agents have several applications, such as theory testing and validation, or as a complement to first stages of drug trials. One possible application is the prediction of the lethality of new Influenza A strains, which are constantly created due to antigenic drift and shift. Here, we present several simulations of antigenic variability in Influenza A using an agent-based approach, where low level molecular antigen-antibody interactions are explicitly described. Antigenic drift and shift events are analyzed regarding the virulence of emergent strains against the IS. Results are discussed from a qualitative point of view taking into account recent and generally recognized immunology and influenza literature

Margarine products quality monitoring using reflectance UV-VIS-SWNIR spectroscopy

Margarine is a term that can indicate any of a wide range of butter substitutes. Due to the great diversity of the raw material, margarine end-product quality characteristics are expected to be highly diversified. This article proposes the use of reflectance UV-VIS-SWNIR spectroscopy to monitor the margarine end-product quality properties. The main effort in this work was the development of a fast monitoring procedure to assess the quality of the manufactured margarines. 
The study was performed on two margarine products: regular margarine (less than 80% fat) and reduced-fat margarine (less than 60% fat). The nine product samples were collected during the production line normal operating conditions on different days. The samples had the surface cleaned in order to remove any sign of oxidized material. Then, spectra were collected by a reflectance probe normal to the sample surface. The samples temperature was recorded (10.0± 2.0ºC) and the probe-sample distance was kept constant for all the samples. The integration time was set to 40s for the collection of the five UV/VIS spectra per samples; the three VIS/NIR spectra per sample were collected using a 10s integration time.
The data analysis was performed on each product and for each spectral range independently. The spectra were normalized by its maximum intensity and the corrected for using a robust multiplicative scatter correction algorithm. A principal component analysis was performed to the pre-process spectra and the multivariate statistical process control limits were determined with bootstrap for each product/spectral range.
Results show that UV-VIS-SWNIR reflectance spectroscopy provides a quick and fast assessment of these products characteristics and thus it can be used as an indication of the overall product variability

Simulation of immune system response to bacterial challenge

Immune system (IS) simulations have several applications, such as biological theory testing or as a complement in the development of improved drugs. This paper presents an agent based approach to simulate the IS response to bacterial infection challenge. The agent simulator is implemented in a discrete time and twodimensional space, and composed by two layers: a) a specialized cellular automata responsible for substance di usion and reactions; and b) the layer where agents move, act and interact. The IS model focuses upon low level cellular receptor interactions, receptor diversity and genetic-ruled agents, aiming to observe and study the resultant emergent behavior. The model reproduces the following IS behavioral characteristics: speci city and specialization, immune memory and vaccine immunization