Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Semivariogram models for estimating fig fly population density throughout the year

The objective of this work was to select semivariogram models to estimate the population density of fig fly (Zaprionus indianus; Diptera: Drosophilidae) throughout the year, using ordinary kriging. Nineteen monitoring sites were demarcated in an area of 8,200 m2, cropped with six fruit tree species: persimmon, citrus, fig, guava, apple, and peach. During a 24 month period, 106 weekly evaluations were done in these sites. The average number of adult fig flies captured weekly per trap, during each month, was subjected to the circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, hole effect, K-Bessel, J-Bessel, and stable semivariogram models, using ordinary kriging interpolation. The models with the best fit were selected by cross-validation. Each data set (months) has a particular spatial dependence structure, which makes it necessary to define specific models of semivariograms in order to enhance the adjustment to the experimental semivariogram. Therefore, it was not possible to determine a standard semivariogram model; instead, six theoretical models were selected: circular, Gaussian, hole effect, K-Bessel, J-Bessel, and stable.

Soins intégrés: comment accompagner un changement de culture [Integrated care: how to support a culture change?].

The primary care center at Lausanne University Hospital trains residents to new models of integrated care. The future GPs discover new forms of collaboration with nurses, pharmacists or social workers. The collaboration model includes seeing patients together or delegating care to other providers, with the aim of improving the efficiency of a patient-centered care approach. The article includes examples of integrated care in consultation for travelers, victims of violence, pharmacist medication adherence counseling, medicosocial team work for alcohol use disorders and nurse practitioners' primary care for asylum seekers.

Predicting chick body mass by artificial intelligence-based models

The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

Field data and prediction models of pesticide spray drift on coffee crop

The objective of this work was to generate drift curves from pesticide applications on coffee plants and to compare them with two European drift-prediction models. The used methodology is based on the ISO 22866 standard. The experimental design was a randomized complete block with ten replicates in a 2x20 split-plot arrangement. The evaluated factors were: two types of nozzles (hollow cone with and without air induction) and 20 parallel distances to the crop line outside of the target area, spaced at 2.5 m. Blotting papers were used as a target and placed in each of the evaluated distances. The spray solution was composed of water+rhodamine B fluorescent tracer at a concentration of 100 mg L-1, for detection by fluorimetry. A spray volume of 400 L ha-1 was applied using a hydropneumatic sprayer. The air-induction nozzle reduces the drift up to 20 m from the treated area. The application with the hollow cone nozzle results in 6.68% maximum drift in the nearest collector of the treated area. The German and Dutch models overestimate the drift at distances closest to the crop, although the Dutch model more closely approximates the drift curves generated by both spray nozzles.

Monte Carlo Methods in Parameter Estimation of Nonlinear Models

Yksi keskeisimmistä tehtävistä matemaattisten mallien tilastollisessa analyysissä on mallien tuntemattomien parametrien estimointi. Tässä diplomityössä ollaan kiinnostuneita tuntemattomien parametrien jakaumista ja niiden muodostamiseen sopivista numeerisista menetelmistä, etenkin tapauksissa, joissa malli on epälineaarinen parametrien suhteen. Erilaisten numeeristen menetelmien osalta pääpaino on Markovin ketju Monte Carlo -menetelmissä (MCMC). Nämä laskentaintensiiviset menetelmät ovat viime aikoina kasvattaneet suosiotaan lähinnä kasvaneen laskentatehon vuoksi. Sekä Markovin ketjujen että Monte Carlo -simuloinnin teoriaa on esitelty työssä siinä määrin, että menetelmien toimivuus saadaan perusteltua. Viime aikoina kehitetyistä menetelmistä tarkastellaan etenkin adaptiivisia MCMC menetelmiä. Työn lähestymistapa on käytännönläheinen ja erilaisia MCMC -menetelmien toteutukseen liittyviä asioita korostetaan. Työn empiirisessä osuudessa tarkastellaan viiden esimerkkimallin tuntemattomien parametrien jakaumaa käyttäen hyväksi teoriaosassa esitettyjä menetelmiä. Mallit kuvaavat kemiallisia reaktioita ja kuvataan tavallisina differentiaaliyhtälöryhminä. Mallit on kerätty kemisteiltä Lappeenrannan teknillisestä yliopistosta ja Åbo Akademista, Turusta.

Oppimistehtaan toimintamallin kehittäminen

Educaworks Oy on toiminut viisi vuotta oppimistehtaana, jonka omistajia ovat olleet yritykset ja oppilaitokset. Yrityksen pääasiallisia työntekijöitä ovat tähän mennessä olleet ammattiopiston työssäoppijat ja parina viimeisenä vuotena on yrityksellä ollut palkkalistoillaan omia työntekijöitä. Tässä työssä luotiin vaihtoehtoja Educaworks Oy:n tulevalle toiminnalle lähtien siitä, että tämä nykyinen toimintamalli on tullut tiensä päähän ja tarvitaan uusi malli toiminnan jatkamiselle.Työssä haettiin hyviä käytäntöjä Suomesta benchmarkingin avulla. Näiden mallienvahvuuksia hyödyntämällä pystyttiin kehittämään vaihtoehto Educaworks Oy:n tulevalle toiminnalle. Tämä malli, oppimistehdas osana osaamiskeskittymää, valittiin toteutettavaksi kolmesta eri vaihtoehdosta, joista kaksi muuta olivat oppimistehdas yrityksenä ja oppimistehdas osana koulutusorganisaatiota. Valitussa vaihtoehdossa hyödynnetään Savonia-ammattikorkeakoulun suunnitteilla olevaa EducaTech Center-hanketta, jossa on tarkoitus luoda Iisalmeen teknologiateollisuuden osaamiskeskittymä seuraavan EU-kauden 2007-2013 aikana. Valitussa mallissa Educaworks Oy hyödyntää tulevassa toiminnassaan tämän osaamiskeskittymän uutta kone- ja laitekantaa sekä tekee yhteistyötä keskittymän tutkimus- ja tuotekehityshenkilökunnan kanssa. Yritykset pääsevät parhaiten osallisiksi Educa Tech Center osaamiskeskittymän tuottamista palveluista hankkimalla Educaworks Oy:n osakkeita ja pääsemällä täten keskittymän ytimeen sen tuotannollisen toimijan, Educaworks Oy:n, avulla. Educaworks Oy toimii tässä keskittymässä komponenttitoimittajan roolissa ollen malli komponenttitoimittajasta muillealueella oleville vastaaville verkostoissa toimiville yrityksille. Educaworks Oy:n toiminnan toisena periaatteena tulee olemaan työssäoppiminen. Työssäoppiminen on tänä päivänä osa ammatillista koulutusta ja sen merkityskorostuu yhä enemmän, koska oppilaat tulevat opiskelemaan tänä päivänä monesti lähtökohdista, joissa heillä ei ole ollut mahdollisuutta harjoittaa käytännön taitojaan ennen ammatillisten opiskelujen aloittamista. Työpaikoilla ei ole vielä kovin hyvää valmiutta toteuttaa sitä opetushallituksen tavoitetta, että oppilaatoppisivat työssäoppimisjaksoilla uusia asioita ohjatusti. Työpaikoilta puuttuu työssäoppimisen ohjaajat ja oppilaiden tekemät harjoitteet ovat liian monta kertaa ammatillisesti kovin vaatimattomia jäysteenpoisto- tai kappaleenvaihtotöitä koneistuksesta puhuttaessa. Tässä työssä luodaan mallia oppilaiden ohjatulle työssäoppimiselle tehtyjen tieteellisten tutkimusten pohjalta. Tavoitteena on, että Educaworks Oy:ssä pystyttäisiin jatkossa kouluttamaan myös muiden alueen teknologiateollisuuden yritysten työntekijöitä toimimaan työssäoppimisen ohjaajina.

Investigation of discrete imaging models and iterative image reconstruction in differential X-ray phase-contrast tomography.

Differential X-ray phase-contrast tomography (DPCT) refers to a class of promising methods for reconstructing the X-ray refractive index distribution of materials that present weak X-ray absorption contrast. The tomographic projection data in DPCT, from which an estimate of the refractive index distribution is reconstructed, correspond to one-dimensional (1D) derivatives of the two-dimensional (2D) Radon transform of the refractive index distribution. There is an important need for the development of iterative image reconstruction methods for DPCT that can yield useful images from few-view projection data, thereby mitigating the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods. In this work, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction in DPCT. We also investigate the use of one of the models with a modern image reconstruction algorithm for performing few-view image reconstruction of a tissue specimen.

Predicting Non Return to Work after Orthopaedic Trauma: The Wallis Occupational Rehabilitation RisK (WORRK) Model.

BACKGROUND: Workers with persistent disabilities after orthopaedic trauma may need occupational rehabilitation. Despite various risk profiles for non-return-to-work (non-RTW), there is no available predictive model. Moreover, injured workers may have various origins (immigrant workers), which may either affect their return to work or their eligibility for research purposes. The aim of this study was to develop and validate a predictive model that estimates the likelihood of non-RTW after occupational rehabilitation using predictors which do not rely on the worker's background. METHODS: Prospective cohort study (3177 participants, native (51%) and immigrant workers (49%)) with two samples: a) Development sample with patients from 2004 to 2007 with Full and Reduced Models, b) External validation of the Reduced Model with patients from 2008 to March 2010. We collected patients' data and biopsychosocial complexity with an observer rated interview (INTERMED). Non-RTW was assessed two years after discharge from the rehabilitation. Discrimination was assessed by the area under the receiver operating curve (AUC) and calibration was evaluated with a calibration plot. The model was reduced with random forests. RESULTS: At 2 years, the non-RTW status was known for 2462 patients (77.5% of the total sample). The prevalence of non-RTW was 50%. The full model (36 items) and the reduced model (19 items) had acceptable discrimination performance (AUC 0.75, 95% CI 0.72 to 0.78 and 0.74, 95% CI 0.71 to 0.76, respectively) and good calibration. For the validation model, the discrimination performance was acceptable (AUC 0.73; 95% CI 0.70 to 0.77) and calibration was also adequate. CONCLUSIONS: Non-RTW may be predicted with a simple model constructed with variables independent of the patient's education and language fluency. This model is useful for all kinds of trauma in order to adjust for case mix and it is applicable to vulnerable populations like immigrant workers.

The Role of an Enterprise Portal in Knowledge Management Strategy, Case: Private Equity Investor

Työ tutkii yritysportaalin roolia organisaation tietojohtamisessa. Tutkimusongelman ratkaisemiseksi luodaan viitekehys, jossa yritysportaalin ja tietojohtamisen teoriat linkittyvät. Työn empiirisessä osassa viitekehys on pohjana case-yritykselle rakennettavalle yritysportaalille. Laadullinen tutkimus käsittää teoriaosuuden sekä osallistuvaan case-tutkimukseen perustuvan empiriaosuuden. Työn runko muodostuu kahden vastakkaisen tietojohtamisajattelun vuoropuhelusta, jotka ovat informaatioteknologiaan- ja strategiseen johtamiseen perustuvat näkökulmat. Toimivan tietojohtamismallin täytyy sisältää molemmat aspektit. Jokainen organisaatio tarvitsee informaation hallintaan liittyviä toiminnallisuuksia ja täten eksplisiittisen tiedon hallinta tietojärjestelmien avulla on onnistuneen tietojohtamisen kulmakiviä. Tätä perusinfrastruktuuria on mahdollista laajentaa hiljaisen tiedon hallintaan perustuvilla tietojohtamismenetelmillä. Työn ratkaisu näiden kahden näkemyksen, 'kovan' informaatioteknogiaan painottuvan sekä 'pehmeän' ihmisnäkökulman integrointiin, on yritysportaali. Työssä käytettävä yritysportaalin viitekehys rakentuu kolmeen päätoiminnallisuuteen; sisällönhallintaan, yhteistyöominaisuuksiin ja liiketoimintatiedon hallintaan. Työ todistaa yhteyden viitekehyksen sekä tietojohtamisen perusmallien, kuten tietojohtamisen prosessimallin sekä tietoympäristöjen välillä. Yritysportaali voi täten toimia, ei ainoastaan yksittäisten tietojohtamistyökalujen implementoinnissa, vaan tietojohtamisstrategian luomisen apuna tarjoten alustan tai 'katalyytin' kokonaisvaltaiselle tietojohtamiselle.

Diversity management and team leadership in a nonprofit organization. Case: AIESEC in Finland

Tutkimuksen tarkoituksena on selvittää kuinka moninaisuus ja sen johtaminen näkyvät voittoa tavoittelemattoman järjestön tiimityössä, kuinka moninaisuus ja tiimityö pystyvät selittämään motiiveja työskennellä voittoa tavoittelemattomassa järjestössä ja mitä tulisi huomioida tiimityön ja tiimin johtajuuden osalta, kun moninaisuus ja voittoa tavoittelemattoman järjestön luonne otetaan huomioon. Tämä tutkielma on laadullinen tutkimus, jossa tutkimusmenetelminä on käytetty yhdeksää teemahaastattelua, edellisen tutkimuksen tuloksia (Astikainen, 2005) sekä havainnointia. Tutkimuksen perusteellavoidaan todeta, että voittoa tavoittelemattoman järjestön luonne, tiimityö tai moninaisuus eivät sinällään merkitse paljoakaan tulosten kannalta, vaan niiden keskinäiset yhteydet. Nämä yhdessä, oikein hyödynnettynä, vaikuttavat työntekijöiden motivaatioon ja sitä kautta organisaation tuloksiin.

Competitor Information andCompetitive Knowledge Management in a Large Industrial Organization

Thisthesis supplements the systematic approach to competitive intelligence and competitor analysis by introducing an information-processing perspective on management of the competitive environment and competitors therein. The cognitive questions connected to the intelligence process and also the means that organizational actors use in sharing information are discussed. The ultimate aim has been to deepen knowledge of the different intraorganizational processes that are used in acorporate organization to manage and exploit the vast amount of competitor information that is received from the environment. Competitor information and competitive knowledge management is examined as a process, where organizational actorsidentify and perceive the competitive environment by using cognitive simplification, make interpretations resulting in learning and finally utilize competitor information and competitive knowledge in their work processes. The sharing of competitive information and competitive knowledge is facilitated by intraorganizational networks that evolve as a means of developing a shared, organizational level knowledge structure and ensuring that the right information is in the right place at the right time. This thesis approaches competitor information and competitive knowledge management both theoretically and empirically. Based on the conceptual framework developed by theoretical elaboration, further understanding of the studied phenomena is sought by an empirical study. The empirical research was carried out in a multinationally operating forest industry company. This thesis makes some preliminary suggestions of improving the competitive intelligence process. It is concluded that managing competitor information and competitive knowledge is not simply a question of managing information flow or improving sophistication of competitor analysis, but the crucial question to be solved is rather, how to improve the cognitive capabilities connected to identifying and making interpretations of the competitive environment and how to increase learning. It is claimed that competitive intelligence can not be treated like an organizational function or assigned solely to a specialized intelligence unit.

Definition of linear color models in the RGB vector color space to detect red peaches in orchard images taken under natural illumination

This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.