Mapping prior information onto LMI eigenvalue-regions for discrete-time subspace identification

In subspace identification, prior information can be used to constrain the eigenvalues of the estimated state-space model by defining corresponding LMI regions. In this paper, first we argue on what kind of practical information can be extracted from historical data or step-response experiments to possibly improve the dynamical properties of the corresponding model and, also, on how to mitigate the effect of the uncertainty on such information. For instance, prior knowledge regarding the overshoot, the period between damped oscillations and settling time may be useful to constraint the possible locations of the eigenvalues of the discrete-time model. Then, we show how to map the prior information onto LMI regions and, when the obtaining regions are non-convex, to obtain convex approximations.Comment: Under revie

Automated Identification and Classification of Stereochemistry: Chirality and Double Bond Stereoisomerism

Stereoisomers have the same molecular formula and the same atom connectivity and their existence can be related to the presence of different three-dimensional arrangements. Stereoisomerism is of great importance in many different fields since the molecular properties and biological effects of the stereoisomers are often significantly different. Most drugs for example, are often composed of a single stereoisomer of a compound, and while one of them may have therapeutic effects on the body, another may be toxic. A challenging task is the automatic detection of stereoisomers using line input specifications such as SMILES or InChI since it requires information about group theory (to distinguish stereoisomers using mathematical information about its symmetry), topology and geometry of the molecule. There are several software packages that include modules to handle stereochemistry, especially the ones to name a chemical structure and/or view, edit and generate chemical structure diagrams. However, there is a lack of software capable of automatically analyzing a molecule represented as a graph and generate a classification of the type of isomerism present in a given atom or bond. Considering the importance of stereoisomerism when comparing chemical structures, this report describes a computer program for analyzing and processing steric information contained in a chemical structure represented as a molecular graph and providing as output a binary classification of the isomer type based on the recommended conventions. Due to the complexity of the underlying issue, specification of stereochemical information is currently limited to explicit stereochemistry and to the two most common types of stereochemistry caused by asymmetry around carbon atoms: chiral atom and double bond. A Webtool to automatically identify and classify stereochemistry is available at http://nams.lasige.di.fc.ul.pt/tools.ph

Atomic jet from SMM1 (FIRS1) in Serpens uncovers non-coeval binary companion

We report on the detection of an atomic jet associated with the protostellar source SMM1 (FIRS1) in Serpens. The jet is revealed in [FeII] and [NeII] line maps observed with Spitzer/IRS, and further confirmed in HiRes IRAC and MIPS images. It is traced very close to SMM1 and peaks at ~5 arcsec" from the source at a position angle of $\sim 125 degrees. In contrast, molecular hydrogen emission becomes prominent at distances > 5" from the protostar and extends at a position angle of 160 degrees. The morphological differences suggest that the atomic emission arises from a companion source, lying in the foreground of the envelope surrounding the embedded protostar SMM1. In addition the molecular and atomic Spitzer maps disentangle the large scale CO (3-2) emission observed in the region into two distinct bipolar outflows, giving further support to a proto-binary source setup. Analysis at the peaks of the [FeII] jet show that emission arises from warm and dense gas (T ~1000 K, n(electron) 10^5 - 10^6 cm^-3). The mass flux of the jet derived independently for the [FeII] and [NeII] lines is 10^7 M(sun)/yr, pointing to a more evolved Class~I/II protostar as the driving source. All existing evidence converge to the conclusion that SMM1 is a non-coeval proto-binary source.Comment: 10 pages, 7 figures, 1 table. Accepted for publication in Astronomy \& Astrophysic

Photospheric properties and fundamental parameters of M dwarfs

M dwarfs are an important source of information when studying and probing the lower end of the Hertzsprung-Russell (HR) diagram, down to the hydrogen-burning limit. Being the most numerous and oldest stars in the galaxy, they carry fundamental information on its chemical history. The presence of molecules in their atmospheres, along with various condensed species, complicates our understanding of their physical properties and thus makes the determination of their fundamental stellar parameters more challenging and difficult. The aim of this study is to perform a detailed spectroscopic analysis of the high-resolution H-band spectra of M dwarfs in order to determine their fundamental stellar parameters and to validate atmospheric models. The present study will also help us to understand various processes, including dust formation and depletion of metals onto dust grains in M dwarf atmospheres. The high spectral resolution also provides a unique opportunity to constrain other chemical and physical processes that occur in a cool atmosphere The high-resolution APOGEE spectra of M dwarfs, covering the entire H-band, provide a unique opportunity to measure their fundamental parameters. We have performed a detailed spectral synthesis by comparing these high-resolution H-band spectra to that of the most recent BT-settl model and have obtained fundamental parameters such as effective temperature, surface gravity, and metallicity (Teff, log g and [Fe/H]) respectively.Comment: 15 pages, 10 figures, accepted for publication in A&

A new approach to modelling and forecasting monthly overnights in the Northern Region of Portugal

The need to analyze the main factors determining the evolution of demand within the tourism sector, which is the driving force of the whole tourism activity, and the importance that forecasting has in this domain, may be justified by the fact that the tourism sector plays a significant role in the economy of Portugal and its regions because of the large number of people employed directly and indirectly, and also because of its ability to bring in currency that reflects in different sector of economic activity. Although tourism is less developed in the North of Portugal than in other regions of the country, it is essential to comprehend this phenomenon in order to empower local economic agents to carry out strategic measures to maximize profits from newly emerging situations. The objective of the present research is to quantify national and international tourism flows by developing (mathematical) models and applying them to sensitivity studies in order to predict demand. This work provides a deeper understanding of the tourism sector in Northern Portugal and contributes to already existing econometric studies by using the Artificial Neural Networks methodology. This work's focus is on the treatment, analysis, and modelling of time series representing “Monthly Guest Nights in Hotels” in Northern Portugal recorded between January 1987 and December 2003. This was achieved through a study of the reference time series whose past values were known and whose objective was to obtain a model that better predicts the behaviour of the time series under study. The model used 6 neurons in the hidden layer with the logistic activation function and was trained using the Resilient Backpropagation algorithm (a variation of backpropagation algorithm). Each time series forecast depended on 12 preceding values. The obtained model yielded acceptable goodness of fit and statistical properties and is therefore adequate for the modelling and prediction of the reference time series

Prediction tourism demand using artificial neural networks

The aim of this research is to quantify the tourism demand using an Artificial Neural Network (ANN) model. The methodology was focused in the treatment, analysis and modulation of the tourism time series: “Monthly Guest Nights in Hotels” in Northern Portugal recorded between January 1987 and December 2006, since it is one of the variables that better explain the effective tourism demand. The model used 4 neurons in the hidden layer with the logistic activation function and was trained using the Resilient Backpropagation algorithm. Each time series forecast depended on 12 preceding values. The developed model yielded acceptable goodness of fit and statistical properties and therefore it is adequate for the modulation and prediction of the reference time series

Jitter, Shimmer and HNR classification within gender, tones and vowels in healthy voices

A statistical analysis of the Jitter, Shimmer and Harmonic to Noise Ratio parameters was applied to classify and compare genders, vowels and tones of healthy voices. Different type of speech records has used for the comparison, namely records with sustained vowels /a/, /i/ and /u/ at High, Low and Neutral tones. A gender comparison has made denoting differences only in Jitter parameter. The parameters determined in recorded vowels /a/, /i/ and /u/ has also compared and the Kruskal Wallis statistical test showed differences for parameters rap, Shim, ShdB, apq3, apq5 and HNR. High, Low and Neutral tones has compared using the same statistical test denoting statistical differences for all Jitter, Shimmer and HNR parameters. A statistical classification of the mean and standard values for these parameters on healthy voices is also presented.info:eu-repo/semantics/publishedVersio

Nova abordagem da metodologia de redes neuronais artificiais para a previsão de séries temporais de turismo: a data com índice. Aplicação à Região da Madeira

Em trabalhos anteriores os autores relataram os seus trabalhos com Redes Neuronais Artificiais (RNA) para realizarem a previsão da série temporal ‘Dormidas Mensais em Hotéis’ das regiões Norte, Centro e Portugal Continental. A metodologia de RNA tem provado fazer previsões com melhor precisão que outras metodologias. Como consequência do aumento da procura turística nos últimos anos, estas séries registaram uma tendência significativamente crescente. Como esta metodologia usa o passado no seu treino tem-se tornado cada vez mais difícil para este modelo prever valores futuros com uma dimensão nunca vista no passado. Os autores propõem neste trabalho uma nova abordagem usando o tempo como parâmetro de entrada em vez de usarem apenas os últimos 12 valores registados no ano anterior. Com este novo parâmetro na entrada pretendem capturar a variação temporal destas séries ao longo dos anos. Neste trabalho foi usada a série temporal da Região Autónoma da Madeira usando o mês e o ano como índices na entrada da RNA em diferentes combinações de acordo com modelos já experimentados com a série da região Norte. Os modelos confirmaram o benefício da utilização dos índices temporais reduzindo o valor do erro relativo médio e também do coeficiente de correlação