Regression analysis of compositional data when both the dependent variable and independent variable are components

It is well known that regression analyses involving compositional data need special attention because the data are not of full rank. For a regression analysis where both the dependent and independent variable are components we propose a transformation of the components emphasizing their role as dependent and independent variables. A simple linear regression can be performed on the transformed components. The regression line can be depicted in a ternary diagram facilitating the interpretation of the analysis in terms of components. An exemple with time-budgets illustrates the method and the graphical features

SQualTrack: A Tool for Robust Fault Detection

One of the techniques used to detect faults in dynamic systems is analytical redundancy. An important difficulty in applying this technique to real systems is dealing with the uncertainties associated with the system itself and with the measurements. In this paper, this uncertainty is taken into account by the use of intervals for the parameters of the model and for the measurements. The method that is proposed in this paper checks the consistency between the system's behavior, obtained from the measurements, and the model's behavior; if they are inconsistent, then there is a fault. The problem of detecting faults is stated as a quantified real constraint satisfaction problem, which can be solved using the modal interval analysis (MIA). MIA is used because it provides powerful tools to extend the calculations over real functions to intervals. To improve the results of the detection of the faults, the simultaneous use of several sliding time windows is proposed. The result of implementing this method is semiqualitative tracking (SQualTrack), a fault-detection tool that is robust in the sense that it does not generate false alarms, i.e., if there are false alarms, they indicate either that the interval model does not represent the system adequately or that the interval measurements do not represent the true values of the variables adequately. SQualTrack is currently being used to detect faults in real processes. Some of these applications using real data have been developed within the European project advanced decision support system for chemical/petrochemical manufacturing processes and are also described in this paper

Fourier-based registration for robust forward-looking sonar mosaicing in low-visibility underwater environments

Vehicle operations in underwater environments are often compromised by poor visibility conditions. For instance, the perception range of optical devices is heavily constrained in turbid waters, thus complicating navigation and mapping tasks in environments such as harbors, bays, or rivers. A new generation of high-definition forward-looking sonars providing acoustic imagery at high frame rates has recently emerged as a promising alternative for working under these challenging conditions. However, the characteristics of the sonar data introduce difficulties in image registration, a key step in mosaicing and motion estimation applications. In this work, we propose the use of a Fourier-based registration technique capable of handling the low resolution, noise, and artifacts associated with sonar image formation. When compared to a state-of-the art region-based technique, our approach shows superior performance in the alignment of both consecutive and nonconsecutive views as well as higher robustness in featureless environments. The method is used to compute pose constraints between sonar frames that, integrated inside a global alignment framework, enable the rendering of consistent acoustic mosaics with high detail and increased resolution. An extensive experimental section is reported showing results in relevant field applications, such as ship hull inspection and harbor mapping

The adequacy of different robust statistical tests in comparing two independent groups

In the current study, we evaluated various robust statistical methods for comparing two independent groups. Two scenarios for simulation were generated: one of equality and another of population mean differences. In each of the scenarios, 33 experimental conditions were used as a function of sample size, standard deviation and asymmetry. For each condition, 5000 replications per group were generated. The results obtained by this study show an adequate type error I rate but not a high power for the confidence intervals. In general, for the two scenarios studied (mean population differences and not mean population differences) in the different conditions analysed, the Mann-Whitney U-test demonstrated strong performance, and a little worse the t-test of Yuen-Welch.

The causal factors of international inequality in CO2 emissions per capita: a regression-based inequality decomposition analysis

This paper uses the possibilities provided by the regression-based inequality decomposition (Fields, 2003) to explore the contribution of different explanatory factors to international inequality in CO2 emissions per capita. In contrast to previous emissions inequality decompositions, which were based on identity relationships (Duro and Padilla, 2006), this methodology does not impose any a priori specific relationship. Thus, it allows an assessment of the contribution to inequality of different relevant variables. In short, the paper appraises the relative contributions of affluence, sectoral composition, demographic factors and climate. The analysis is applied to selected years of the period 1993–2007. The results show the important (though decreasing) share of the contribution of demographic factors, as well as a significant contribution of affluence and sectoral composition.

Workplace Social Capital and Employee Health

Työyhteisön sosiaalinen pääoma ja työntekijöiden terveys Monien tutkimusten mukaan sosiaalinen pääoma vaikuttaa terveyteen. Vaikka työssä käyvä väestönosa on merkittävän osan valveillaoloajastaan työyhteisössä, siellä kertyvää sosiaalista pääomaa on toistaiseksi tutkittu vähän. Tässä tutkimuksessa selvitettiin työyhteisön sosiaalisen pääoman ja kuntatyöntekijöiden terveyden välistä yhteyttä pitkittäisasetelmassa hyödyntäen Kuntasektorin henkilöstön seurantatutkimuksen aineistoa vuosilta 2000–2005. Yhteensä 48592 kuntatyöntekijää vastasi kyselyyn vuosina 2000–02 (vastausprosentti 68 %). Heistä 35914 (77 %) osallistui myös seurantatutkimukseen vuosina 2004–05. Tutkimuksessa kehitettiin kyselyyn perustuva työyhteisön sosiaalisen pääoman mittausmenetelmä. Työntekijän omaan arvioon perustuvan sosiaalisen pääoman lisäksi mitattiin työyhteisön sosiaalista pääomaa käyttämällä samassa työyhteisössä työskentelevien muiden työntekijöiden keskimääräistä arviota sosiaalisesta pääomasta. Terveyttä mitattiin kysymyksellä koetusta terveydestä. Masennusta arvioitiin sekä kysymällä lääkärin toteamasta masennuksesta että masennuslääkeostoilla Kelan lääkerekistereistä. Analyyseihin otettiin mukaan vain ne kuntatyöntekijät, jotka olivat lähtötilanteissa terveitä eli kokivat terveytensä hyväksi tai heillä ei ollut aiempaa diagnosoitua tai lääkehoitoa vaatinutta masennusta. Tulosten analysointiin käytettiin monitasomallinnusta. Tulokset vakioitiin sosiodemografisten tekijöiden ja terveyskäyttäytymisen suhteen. Neljän vuoden seurannassa sekä jatkuvasti vähäinen että vähenevä yksilön sosiaalinen pääoma työssä lisäsi riskiä koetun terveyden heikkenemiseen niillä kuntatyöntekijöillä, jotka eivät vaihtaneet työpaikkaa seurannan aikana ja jotka seurannan alussa kokivat terveytensä hyväksi. Tulos ei selittynyt sosiodemografisilla tekijöillä tai terveyskäyttäytymisen eroilla. Tuloksen merkittävyyttä tuki havainto, että myös työtoverien arvioon perustuva sosiaalinen pääoma ennusti oman terveyden huononemista seuranta-aikana. Niillä työntekijöillä, jotka työskentelivät sellaisissa työyhteisöissä, joissa koko seurannan ajan oli vähiten sosiaalista pääomaa, oli lähes 1.3 -kertainen riski terveyden heikentymiseen. Vähäinen omaan arvioon perustuva sosiaalinen pääoma työssä ennusti myös masennuksen ilmaantuvuutta lähtötilanteessa ei-masentuneilla lähes neljän vuoden seurannassa. Matalaan sosiaaliseen pääomaan liittyi 20–50 % suurempi todennäköisyys sairastua masennukseen seurannan aikana niin itseraportoidun lääkärin totea-man masennuksen kuin masennuslääkeostojen perusteella. Tätä tulosta ei kuitenkaan pystytty toistamaan käyttämällä oman arvion sijasta työtoverien arviota työyhteisön sosiaalisesta pääomasta. Tutkimusta sosiaalisen pääoman vaikutusta masennuksen ilmaantumiseen jatkettiin selvittämällä miten sosiaalisen pääoman eri ulottuvuudet vaikuttivat masennuksen ilmaantumiseen. Tulosten mukaan sosiaalisen pääoman vertikaalinen komponentti (työntekijöiden ja esimiesten välinen luottamus, vastavuoroisuus ja jaetut arvot ja normit, jotka edesauttavat yhteistyötä) sekä horisontaalinen komponentti (työntekijöiden välisissä suhteissa yhteistyöstä, luottamuksesta ja vastavuoroisuudesta syntyvä sosiaalinen pääoma) vaikuttivat itsenäisesti masennusriskiin. Tutkimuksen perusteella korkea työyhteisön sosiaalinen pääoma saattaa vaikuttaa edullisesti työntekijöiden terveyteen. Jos näin on, olisi tärkeää edistää työyhteisöjen sosiaalista pääomaa ja kannustaa sellaiseen toimintaan, joka lisää suvaitsevaisuutta, luottamusta ja vastavuoroisuutta sekä työntekijöiden kesken että työntekijöiden ja esimiesten välillä.

Review of Robust Cooperative Spectrum Sensing Techniques for Cognitive Radio Networks

Cognitive radio networks sense spectrum occupancy and manage themselvesto operate in unused bands without disturbing licensed users. The detection capability of aradio system can be enhanced if the sensing process is performed jointly by a group of nodesso that the effects of wireless fading and shadowing can be minimized. However, taking acollaborative approach poses new security threats to the system as nodes can report falsesensing data to reach a wrong decision. This paper makes a review of secure cooperativespectrum sensing in cognitive radio networks. The main objective of these protocols is toprovide an accurate resolution about the availability of some spectrum channels, ensuring thecontribution from incapable users as well as malicious ones is discarded. Issues, advantagesand disadvantages of such protocols are investigated and summarized.

Estudos de QSAR 3D para um conjunto de inibidores de butirilcolinesterase humana

Alzheimer's disease (AD) is considered the main cause of cognitive decline in adults. The available therapies for AD treatment seek to maintain the activity of cholinergic system through the inhibition of the enzyme acetylcholinesterase. However, butyrylcholinesterase (BuChE) can be considered an alternative target for AD treatment. Aiming at developing new BuChE inhibitors, robust QSAR 3D models with high predictive power were developed. The best model presents a good fit (r²=0.82, q²=0.76, with two PCs) and high predictive power (r²predict=0.88). Analysis of regression vector shows that steric properties have considerable importance to the inhibition of the BuChE.

A sensitive and robust lc-ms/ms method with monolithic column and electrospray ionization for the quantitation of efavirenz in human plasma: application to a bioequivalence study

An LC-MS/MS method has been developed for the determination of efavirenz (EFZ) in human plasma using hydrochlorothiazide as internal standard (I.S.). An ESI negative mode with multiple reaction-monitoring was used monitoring the transitions m/z 313.88→69.24 (EFZ) and 296.02→204.76 (I.S.). Samples were extracted using liquid-liquid extraction. The total run time was 2.0 min. The separation was achieved with HPLC-RP using a monolithic column. The assay was linear in the concentration range of 100 - 5000 ng mL-1. The mean recovery was 83%. Intra- and inter-day precision were < 9.5% and < 8.9%, respectively and accuracy was in the range ± 8.33%. The method was successfully applied to a bioequivalence study.

Optimization of wood flour acetylation by factorial design and partial least squares regression

Acetylation was performed to reduce the polarity of wood and increase its compatibility with polymer matrices for the production of composites. These reactions were performed first as a function of acetic acid and anhydride concentration in a mixture catalyzed by sulfuric acid. A concentration of 50%/50% (v/v) of acetic acid and anhydride was found to produced the highest conversion rate between the functional groups. After these reactions, the kinetics were investigated by varying times and temperatures using a 3² factorial design, and showed time was the most relevant parameter in determining the conversion of hydroxyl into carbonyl groups.

Least squares regression with errors in both variables: case studies

Analytical curves are normally obtained from discrete data by least squares regression. The least squares regression of data involving significant error in both x and y values should not be implemented by ordinary least squares (OLS). In this work, the use of orthogonal distance regression (ODR) is discussed as an alternative approach in order to take into account the error in the x variable. Four examples are presented to illustrate deviation between the results from both regression methods. The examples studied show that, in some situations, ODR coefficients must substitute for those of OLS, and, in other situations, the difference is not significant.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

The increasing demand of consumer markets for the welfare of birds in poultry house has motivated many scientific researches to monitor and classify the welfare according to the production environment. Given the complexity between the birds and the environment of the aviary, the correct interpretation of the conduct becomes an important way to estimate the welfare of these birds. This study obtained multiple logistic regression models with capacity of estimating the welfare of broiler breeders in relation to the environment of the aviaries and behaviors expressed by the birds. In the experiment, were observed several behaviors expressed by breeders housed in a climatic chamber under controlled temperatures and three different ammonia concentrations from the air monitored daily. From the analysis of the data it was obtained two logistic regression models, of which the first model uses a value of ammonia concentration measured by unit and the second model uses a binary value to classify the ammonia concentration that is assigned by a person through his olfactory perception. The analysis showed that both models classified the broiler breeder's welfare successfully.

Adjust of regression models to estimate the rectal temperature of broilers for the first 14 days of life

The broiler rectal temperature (t rectal) is one of the most important physiological responses to classify the animal thermal comfort. Therefore, the aim of this study was to adjust regression models in order to predict the rectal temperature (t rectal) of broiler chickens under different thermal conditions based on age (A) and a meteorological variable (air temperature - t air) or a thermal comfort index (temperature and humidity index -THI or black globe humidity index - BGHI) or a physical quantity enthalpy (H). In addition, through the inversion of these models and the expected t rectal intervals for each age, the comfort limits of t air, THI, BGHI and H for the chicks in the heating phase were determined, aiding in the validation of the equations and the preliminary limits for H. The experimental data used to adjust the mathematical models were collected in two commercial poultry farms, with Cobb chicks, from 1 to 14 days of age. It was possible to predict the t rectal of conditions from the expected t rectal and determine the lower and superior comfort thresholds of broilers satisfactorily by applying the four models adjusted; as well as to invert the models for prediction of the environmental H for the chicks first 14 days of life.