Spacecraft's attitude prediction: solar radiation torque and the Earth's shadow

A semi-analytical approach is proposed to study the rotational motion of an artificial satellite, under the influence of torque due to the solar radiation pressure, and taking into account the influence of Earth's shadow. Using Andoyer variables the equations for the rotational motion are presented in extended Hamiltonian form. In order to get a solution for the state variables close to an actual motion, the considered model for the shadow function takes into account physical and geometric factors and three specific regions: shadow, penumbra and full light. A mapping for the shadow function is proposed and a semi-analytical process is applied. When the satellite is totally illuminated or it is inside the penumbra, a known analytical solution is used to compute the satellite's attitude. A numerical simulation shows, when the penumbra region is included, the attenuation of the rotational motion during the transition from the shadow to the illuminate region and vice versa. (c) 2005 Published by Elsevier Ltd on behalf of COSPAR.

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened Steel turning

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

Bothrops moojeni myotoxin-II, a Lys49-phospholipase A(2) homologue: An example of function versatility of snake venom proteins

MjTX-II, a myotoxic phospholipase A(2) (PLA(2)) homologue from Bothrops moojeni venom, was functionally and structurally characterized. The MjTX-II characterization included: (i) functional characterization (antitumoral, antimicrobial and antiparasitic effects); (ii) effects of structural modifications by 4-bromophenacyl bromide (BPB), cyanogen bromide (CNBr), acetic anhydride and 2-nitrobenzenesulphonyl fluoride (NBSF); (iii) enzymatic characterization: inhibition by low molecular weight heparin and EDTA; and (iv) molecular characterization: cDNA sequence and molecular structure prediction. The results demonstrated that MjTX-II displayed antimicrobial activity by growth inhibition against Escherichia coli and Candida albicans, antitumoral activity against Erlich ascitic tumor (EAT), human breast adenocarcinoma (SK-BR-3) and human T leukemia cells (JURKAT) and antiparasitic effects against Schistosoma mansoni and Leishmania spp., which makes MjTX-II a promising molecular model for future therapeutic applications, as well as other multifunctional homologous Lys49-PLA(2)S or even derived peptides. This work provides useful insights into the structural determinants of the action of Lys49-PLA2 homologues and, together with additional strategies, supports the concept of the presence of others bioactive sites distinct from the catalytic site in snake venom myotoxic PLA(2)s. (c) 2005 Elsevier B.V. All rights reserved.

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multivariate calibrations on H-1 NMR profiles for prediction of physicochemical parameters of Brazilian commercial gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Three-body recombination in ultracold systems: Prediction of weakly-bound atomic trimer energies

The three-body recombination coefficient of an ultracold atomic system, together with the corresponding two-body scattering length a, allow us to predict the energy E 3 of the shallow trimer bound state, using a universal scaling function. The production of dimers in trapped Bose-Einstein condensates, from three-body recombination processes, in the regime of short magnetic pulses near a Feshbach resonance, is also studied in line with the experimental observation.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

On the prediction of pickup and saltation velocities in pneumatic conveying

This paper presents a comparative study of the critical pickup and saltation velocities of particles in horizontal pipelines for pneumatic conveying design. A comparative study is performed using different existing correlations in the literature for the determination of the minimum velocity of transport as a function of the particle and pipe diameter, particle density, solid mass flow rate and particle sphericity. Their limitations and difficulties in predicting those critical velocities are analyzed. For the pickup velocity, an experimental study was also carried out in order to support the analysis. Recommendations are presented on the use of such correlations.

Magnetic susceptibility in the prediction of soil attributes in two sugarcane harvesting management systems

This study aimed to investigate the potential use of magnetic susceptibility (MS) as pedotransfer function to predict soil attributes under two sugarcane harvesting management systems. For each area of 1 ha (one with green sugarcane mechanized harvesting and other one with burnt sugarcane manual harvesting), 126 soil samples were collected and subjected to laboratory analysis to determine soil physical, chemical and mineralogical attributes and for measuring of MS. Data were submitted to descriptive statistics by calculating the mean and coefficient of variation. In order to compare the means in the different harvesting management systems it was carried out the Tukey test at a significance level of 5%. In order to investigate the correlation of the MS with other soil properties it was made the correlation test and aiming to assess how the MS contributes to the prediction of soil complex attributes it was made the multiple linear regressions. The results demonstrate that MS showed, in both sugarcane harvesting management systems, statistical correlation with chemical, physical and mineralogical soil attributes and it also showed potential to be used as pedotransfer function to predict attributes of the studied oxisol.

Prediction of dopant atom distribution on nanocrystals using thermodynamic arguments

A theoretical approach aiming at the prediction of segregation of dopant atoms on nanocrystalline systems is discussed here. It considers the free energy minimization argument in order to provide the most likely dopant distribution as a function of the total doping level. For this, it requires as input (i) a fixed polyhedral geometry with defined facets, and (ii) a set of functions that describe the surface energy as a function of dopant content for different crystallographic planes. Two Sb-doped SnO2 nanocrystalline systems with different morphology and dopant content were selected as a case study, and the calculation of the dopant distributions expected for them is presented in detail. The obtained results were compared to previously reported characterization of this system by a combination of HRTEM and surface energy calculations, and both methods are shown to be equivalent. Considering its application pre-requisites, the present theoretical approach can provide a first estimation of doping atom distribution for a wide range of nanocrystalline systems. We expect that its use will support the reduction of experimental effort for the characterization of doped nanocrystals, and also provide a solution to the characterization of systems where even state-of-art analytical techniques are limited.

Prediction of apparent protein digestibility of ingredients and diets by in vitro pH-stat degree of protein hydrolysis with species-specific enzymes for juvenile Pacific white shrimp Litopenaeus vannamei

Aquafeed production faces global issues related to availability of feed ingredients. Feed manufacturers require greater flexibility in order to develop nutritional and cost-effective formulations that take into account nutrient content and availability of ingredients. The search for appropriate ingredients requires detailed screening of their potential nutritional value and variability at the industrial level. In vitro digestion of feedstuffs by enzymes extracted from the target species has been correlated with apparent protein digestibility (APD) in fish and shrimp species. The present study verified the relationship between APD and in vitro degree of protein hydrolysis (DH) with Litopenaeus vannamei hepatopancreas enzymes in several different ingredients (n = 26): blood meals, casein, corn gluten meal, crab meal, distiller`s dried grains with solubles, feather meal, fish meals, gelatin, krill meals, poultry by-product meal, soybean meals, squid meals and wheat gluten. The relationship between APD and DH was further verified in diets formulated with these ingredients at 30% inclusion into a reference diet. APD was determined in vivo (30.1 +/- 0.5 degrees C, 32.2 +/- 0.4%.) with juvenile L vannamei (9 to 12 g) after placement of test ingredients into a reference diet (35 g kg(-1) CP: 8.03 g kg(-1) lipid; 2.01 kcal g(-1)) with chromic oxide as the inert marker. In vitro DH was assessed in ingredients and diets with standardized hepatopancreas enzymes extracted from pond-reared shrimp. The DH of ingredients was determined under different assay conditions to check for the most suitable in vitro protocol for APD prediction: different batches of enzyme extracts (HPf5 or HPf6), temperatures (25 or 30 degrees C) and enzyme activity (azocasein): crude protein ratios (4 U: 80 mg CP or 4 U: 40 mg CP). DH was not affected by ingredient proximate composition. APD was significantly correlated to DH in regressions considering either ingredients or diets. The relationships between APD and DH of the ingredients could be suitably adjusted to a Rational Function (y = (a + bx)/(1 + cx + dx2), n = 26. Best in vitro APD predictions were obtained at 25 degrees C, 4 U: 80 mg CP both for ingredients (R(2) = 0.86: P = 0.001) and test diets (R(2) = 0.96; P = 0.007). The regression model including all 26 ingredients generated higher prediction residuals (i.e., predicted APD - determined APD) for corn gluten meal, feather meal. poultry by-product meal and krill flour. The remaining test ingredients presented mean prediction residuals of 3.5 points. A model including only ingredients with APD>80% showed higher prediction precision (R(2) = 0.98: P = 0.000004; n = 20) with average residual of 1.8 points. Predictive models including only ingredients from the same origin (e.g., marine-based, R(2) = 0.98; P = 0.033) also displayed low residuals. Since in vitro techniques have been usually validated through regressions against in vivo APD, the DH predictive capacity may depend on the consistency of the in vivo methodology. Regressions between APD and DH suggested a close relationship between peptide bond breakage by hepatopancreas digestive proteases and the apparent nitrogen assimilation in shrimp, and this may be a useful tool to provide rapid nutritional information. (C) 2009 Elsevier B.V. All rights reserved.

Induction of Psychosis by Delta 9-Tetrahydrocannabinol Reflects Modulation of Prefrontal and Striatal Function During Attentional Salience Processing

Context: The aberrant processing of salience is thought to be a fundamental factor underlying psychosis. Cannabis can induce acute psychotic symptoms, and its chronic use may increase the risk of schizophrenia. We investigated whether its psychotic effects are mediated through an influence on attentional salience processing. Objective: To examine the effects of Delta 9-tetrahydrocannabinol (Delta 9-THC) and cannabidiol (CBD) on regional brain function during salience processing. Design: Volunteers were studied using event-related functional magnetic resonance imaging on 3 occasions after administration of Delta 9-THC, CBD, or placebo while performing a visual oddball detection paradigm that involved allocation of attention to infrequent (oddball) stimuli within a string of frequent (standard) stimuli. Setting: University center. Participants: Fifteen healthy men with minimal previous cannabis use. Main Outcome Measures: Symptom ratings, task performance, and regional brain activation. Results: During the processing of oddball stimuli, relative to placebo, Delta 9-THC attenuated activation in the right caudate but augmented it in the right prefrontal cortex. Delta 9-Tetrahydrocannabinol also reduced the response latency to standard relative to oddball stimuli. The effect of Delta 9-THC in the right caudate was negatively correlated with the severity of the psychotic symptoms it induced and its effect on response latency. The effects of CBD on task-related activation were in the opposite direction of those of Delta 9-THC; relative to placebo, CBD augmented left caudate and hippocampal activation but attenuated right prefrontal activation. Conclusions: Delta 9-Tetrahydrocannabinol and CBD differentially modulate prefrontal, striatal, and hippocampal function during attentional salience processing. These effects may contribute to the effects of cannabis on psychotic symptoms and on the risk of psychotic disorders.

Computational methods for the analysis of protein structure and function

The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

A QoS Controller Framework Compliant with the ETSI Network Function Virtualization Specification

The 5th generation of mobile networking introduces the concept of “Network slicing”, the network will be “sliced” horizontally, each slice will be compliant with different requirements in terms of network parameters such as bandwidth, latency. This technology is built on logical instead of physical resources, relies on virtual network as main concept to retrieve a logical resource. The Network Function Virtualisation provides the concept of logical resources for a virtual network function, enabling the concept virtual network; it relies on the Software Defined Networking as main technology to realize the virtual network as resource, it also define the concept of virtual network infrastructure with all components needed to enable the network slicing requirements. SDN itself uses cloud computing technology to realize the virtual network infrastructure, NFV uses also the virtual computing resources to enable the deployment of virtual network function instead of having custom hardware and software for each network function. The key of network slicing is the differentiation of slice in terms of Quality of Services parameters, which relies on the possibility to enable QoS management in cloud computing environment. The QoS in cloud computing denotes level of performances, reliability and availability offered. QoS is fundamental for cloud users, who expect providers to deliver the advertised quality characteristics, and for cloud providers, who need to find the right tradeoff between QoS levels that has possible to offer and operational costs. While QoS properties has received constant attention before the advent of cloud computing, performance heterogeneity and resource isolation mechanisms of cloud platforms have significantly complicated QoS analysis and deploying, prediction, and assurance. This is prompting several researchers to investigate automated QoS management methods that can leverage the high programmability of hardware and software resources in the cloud.