Very high-resolution environmental predictors in species distribution models: moving beyond topography?

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species' micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions - and therefore local management - compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

"Suddenly I get into the zone": Examining discontinuities and nonlinear changes in flow experiences at work

Work-related flow is defined as a sudden and enjoyable merging of action and awareness that represents a peak experience in the daily lives of workers. Employees" perceptions of challenge and skill and their subjective experiences in terms of enjoyment, interest and absorption were measured using the experience sampling method, yielding a total of 6981 observations from a sample of 60 employees. Linear and nonlinear approaches were applied in order to model both continuous and sudden changes. According to the R2, AICc and BIC indexes, the nonlinear dynamical systems model (i.e. cusp catastrophe model) fit the data better than the linear and logistic regression models. Likewise, the cusp catastrophe model appears to be especially powerful for modelling those cases of high levels of flow. Overall, flow represents a nonequilibrium condition that combines continuous and abrupt changes across time. Research and intervention efforts concerned with this process should focus on the variable of challenge, which, according to our study, appears to play a key role in the abrupt changes observed in work-related flow.

Parametric Approach to Blind Deconvolution of Nonlinear Channels

A parametric procedure for the blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Experiments show that the proposed algorithms perform efficiently, even in the presence of hard distortion. The method, based on the minimization of the output mutual information, needs the knowledge of log-derivative of input distribution (the so-called score function). Each algorithm consists of three adaptive blocks: one devoted to adaptive estimation of the score function, and two other blocks estimating the inverses of the linear and nonlinear parts of the channel, (quasi-)optimally adapted using the estimated score functions. This paper is mainly concerned by the nonlinear part, for which we propose two parametric models, the first based on a polynomial model and the second on a neural network, while [14, 15] proposed non-parametric approaches.

Post-Nonlinear Mixtures and Beyond

Although sources in general nonlinear mixturm arc not separable iising only statistical independence, a special and realistic case of nonlinear mixtnres, the post nonlinear (PNL) mixture is separable choosing a suited separating system. Then, a natural approach is based on the estimation of tho separating Bystem parameters by minimizing an indcpendence criterion, like estimated mwce mutual information. This class of methods requires higher (than 2) order statistics, and cannot separate Gaarsian sources. However, use of [weak) prior, like source temporal correlation or nonstationarity, leads to other source separation Jgw rithms, which are able to separate Gaussian sourra, and can even, for a few of them, works with second-order statistics. Recently, modeling time correlated s011rces by Markov models, we propose vcry efficient algorithms hmed on minimization of the conditional mutual information. Currently, using the prior of temporally correlated sources, we investigate the fesihility of inverting PNL mixtures with non-bijectiw non-liacarities, like quadratic functions. In this paper, we review the main ICA and BSS results for riunlinear mixtures, present PNL models and algorithms, and finish with advanced resutts using temporally correlated snu~sm

Simulating maize phenology as a function of air temperature with a linear and a nonlinear model

The objective of this study was to adapt a nonlinear model (Wang and Engel - WE) for simulating the phenology of maize (Zea mays L.), and to evaluate this model and a linear one (thermal time), in order to predict developmental stages of a field-grown maize variety. A field experiment, during 2005/2006 and 2006/2007 was conducted in Santa Maria, RS, Brazil, in two growing seasons, with seven sowing dates each. Dates of emergence, silking, and physiological maturity of the maize variety BRS Missões were recorded in six replications in each sowing date. Data collected in 2005/2006 growing season were used to estimate the coefficients of the two models, and data collected in the 2006/2007 growing season were used as independent data set for model evaluations. The nonlinear WE model accurately predicted the date of silking and physiological maturity, and had a lower root mean square error (RMSE) than the linear (thermal time) model. The overall RMSE for silking and physiological maturity was 2.7 and 4.8 days with WE model, and 5.6 and 8.3 days with thermal time model, respectively.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Models of social networks based on social distance attachment

We propose a class of models of social network formation based on a mathematical abstraction of the concept of social distance. Social distance attachment is represented by the tendency of peers to establish acquaintances via a decreasing function of the relative distance in a representative social space. We derive analytical results (corroborated by extensive numerical simulations), showing that the model reproduces the main statistical characteristics of real social networks: large clustering coefficient, positive degree correlations, and the emergence of a hierarchy of communities. The model is confronted with the social network formed by people that shares confidential information using the Pretty Good Privacy (PGP) encryption algorithm, the so-called web of trust of PGP.

Investigation of AASHTOWare Pavement ME Design/DARWin-ME Performance Prediction Models for Iowa Pavement Analysis and Design, 2015

The Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed under National Cooperative Highway Research Program (NCHRP) Project 1-37A as a novel mechanistic-empirical procedure for the analysis and design of pavements. The MEPDG was subsequently supported by AASHTO’s DARWin-ME and most recently marketed as AASHTOWare Pavement ME Design software as of February 2013. Although the core design process and computational engine have remained the same over the years, some enhancements to the pavement performance prediction models have been implemented along with other documented changes as the MEPDG transitioned to AASHTOWare Pavement ME Design software. Preliminary studies were carried out to determine possible differences between AASHTOWare Pavement ME Design, MEPDG (version 1.1), and DARWin-ME (version 1.1) performance predictions for new jointed plain concrete pavement (JPCP), new hot mix asphalt (HMA), and HMA over JPCP systems. Differences were indeed observed between the pavement performance predictions produced by these different software versions. Further investigation was needed to verify these differences and to evaluate whether identified local calibration factors from the latest MEPDG (version 1.1) were acceptable for use with the latest version (version 2.1.24) of AASHTOWare Pavement ME Design at the time this research was conducted. Therefore, the primary objective of this research was to examine AASHTOWare Pavement ME Design performance predictions using previously identified MEPDG calibration factors (through InTrans Project 11-401) and, if needed, refine the local calibration coefficients of AASHTOWare Pavement ME Design pavement performance predictions for Iowa pavement systems using linear and nonlinear optimization procedures. A total of 130 representative sections across Iowa consisting of JPCP, new HMA, and HMA over JPCP sections were used. The local calibration results of AASHTOWare Pavement ME Design are presented and compared with national and locally calibrated MEPDG models.

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.

Forecasting tourism demand to Catalonia: neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourismdemand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time seriesmethods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals fromall the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour,we also find that forecasts of tourist arrivals aremore accurate than forecasts of overnight stays.

Forecasting tourism demand using consumer expectations

Abstract Purpose- There is a lack of studies on tourism demand forecasting that use non-linear models. The aim of this paper is to introduce consumer expectations in time-series models in order to analyse their usefulness to forecast tourism demand. Design/methodology/approach- The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the UK, Germany and Italy) combining qualitative information with quantitative models: autoregressive (AR), autoregressive integrated moving average (ARIMA), self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. The forecasting performance of the different models is evaluated for different time horizons (one, two, three, six and 12 months). Findings- Although some differences are found between the results obtained for the different countries, when comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models. In all cases, forecasts of arrivals show lower root mean square errors (RMSE) than forecasts of overnight stays. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed. Research limitations/implications- This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting. Originality/value- This is the first study on tourism demand focusing specifically on Catalonia. To date, there have been no studies on tourism demand forecasting that use non-linear models such as self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. This paper fills this gap and analyses forecasting performance at a regional level. Keywords Tourism, Forecasting, Consumers, Spain, Demand management Paper type Research paper

Disentangling the formation of contrasting tree line physiognomies combining model selection and Bayesian parameterization for simulation models.

Alpine tree-line ecotones are characterized by marked changes at small spatial scales that may result in a variety of physiognomies. A set of alternative individual-based models was tested with data from four contrasting Pinus uncinata ecotones in the central Spanish Pyrenees to reveal the minimal subset of processes required for tree-line formation. A Bayesian approach combined with Markov chain Monte Carlo methods was employed to obtain the posterior distribution of model parameters, allowing the use of model selection procedures. The main features of real tree lines emerged only in models considering nonlinear responses in individual rates of growth or mortality with respect to the altitudinal gradient. Variation in tree-line physiognomy reflected mainly changes in the relative importance of these nonlinear responses, while other processes, such as dispersal limitation and facilitation, played a secondary role. Different nonlinear responses also determined the presence or absence of krummholz, in agreement with recent findings highlighting a different response of diffuse and abrupt or krummholz tree lines to climate change. The method presented here can be widely applied in individual-based simulation models and will turn model selection and evaluation in this type of models into a more transparent, effective, and efficient exercise.

Customer Expectations and Organizational Buying Behavior in the Flexible Packaging Markets

Tämä työ on tehty yhteistyössä kansainvälisen metsäteollisuusyrityksen Stora Enson kanssa. Työ on osa Stora Enson strategiaa kehitettäessä uusia ja innovatiivisia pakkausmateriaaleja ja ratkaisuja joustopakkausmarkkinoille. Työn päätavoitteina oli selvittää, millaisia odotuksia tuotemerkkienomistajilla ja jatkojalostajilla on joustopakkauksista ja kuinka pakkausten ostopäätökset syntyvät monitahoisessa liikeympäristössä. Työn teoreettinen viitekehys jaettiin kahteen osaan. Asiakasodotuksia lähestyttiin tutkimalla eri palvelu- ja tuotelaadun ulottuvuuksia, ja teollisuuden ostokäyttäytymistä tutkittiin organisaatioiden ostokäyttämistä kuvaavien mallien avulla. Työn empiirisessä osuudessa käytettiin laadullista tutkimusmenetelmää käsittäen asiakashaastatteluja lähinnä Yhdysvalloissa. Haastateltavat yritykset koostuivat maailman johtavista kuluttajatuotteita valmistavista yrityksistä sekä jatkojalostajista. Tutkimustulosten mukaan odotukset pakkauksista liittyvät lähinnä tuotteen suojaamiseen sekä myynnin edistämiseen. Pääodotuksina on myös saada mahdollisimman edullisia papereita, mahdollisimman hyvillä barrier- ja paino-ominaisuuksilla. Tutkimustulokset osoittavat myös, että paperiyhtiöt ovat epäonnistuneet tekemään itseään tunnetuksi teollisuudelle ja heidän odotetaan olevan tulevaisuudessa aggressiivisempia ja innovatiivisempia. Tuotemerkkienomistajat ostavat pakkaukset ja pakkausmateriaalit normaalisti mieluiten jatkojalostajiensa kautta, mutta silti he toivovat yhteistyötä paperintoimittajien kanssa kunhan vain myös jatkojalostajat sisällytetään toimintaan mukaan.

Structure-Based, Rational Design of T Cell Receptors.

Adoptive cell transfer using engineered T cells is emerging as a promising treatment for metastatic melanoma. Such an approach allows one to introduce T cell receptor (TCR) modifications that, while maintaining the specificity for the targeted antigen, can enhance the binding and kinetic parameters for the interaction with peptides (p) bound to major histocompatibility complexes (MHC). Using the well-characterized 2C TCR/SIYR/H-2K(b) structure as a model system, we demonstrated that a binding free energy decomposition based on the MM-GBSA approach provides a detailed and reliable description of the TCR/pMHC interactions at the structural and thermodynamic levels. Starting from this result, we developed a new structure-based approach, to rationally design new TCR sequences, and applied it to the BC1 TCR targeting the HLA-A2 restricted NY-ESO-1157-165 cancer-testis epitope. Fifty-four percent of the designed sequence replacements exhibited improved pMHC binding as compared to the native TCR, with up to 150-fold increase in affinity, while preserving specificity. Genetically engineered CD8(+) T cells expressing these modified TCRs showed an improved functional activity compared to those expressing BC1 TCR. We measured maximum levels of activities for TCRs within the upper limit of natural affinity, K D = ∼1 - 5 μM. Beyond the affinity threshold at K D < 1 μM we observed an attenuation in cellular function, in line with the "half-life" model of T cell activation. Our computer-aided protein-engineering approach requires the 3D-structure of the TCR-pMHC complex of interest, which can be obtained from X-ray crystallography. We have also developed a homology modeling-based approach, TCRep 3D, to obtain accurate structural models of any TCR-pMHC complexes when experimental data is not available. Since the accuracy of the models depends on the prediction of the TCR orientation over pMHC, we have complemented the approach with a simplified rigid method to predict this orientation and successfully assessed it using all non-redundant TCR-pMHC crystal structures available. These methods potentially extend the use of our TCR engineering method to entire TCR repertoires for which no X-ray structure is available. We have also performed a steered molecular dynamics study of the unbinding of the TCR-pMHC complex to get a better understanding of how TCRs interact with pMHCs. This entire rational TCR design pipeline is now being used to produce rationally optimized TCRs for adoptive cell therapies of stage IV melanoma.

Äitiys kulttuurisina odotuksina

Tutkimuksen kohteena ovat äitiydelle tuotetut kulttuuriset odotukset, joita tarkastellaan kahdella yhteiskunnallisella keskustelufoorumilla. Tutkimuksessa tarkastellaan yhtäältä lastensuojelun perhetyössä toimivien ammattilaisten ja toisaalta median puhetta äitiydestä. Tutkimuksen tavoitteena on tehdä näkyväksi vaihtoehtoisia tapoja konstruoida äitiyttä hyvänä tai riittämättömänä sekä haastaa pohtimaan erilaisten tulkintojen perusteita ja seurauksia lastensuojelutyössä. Kulttuuriset, äitiyttä koskevat odotukset vaikuttavat myös siihen, miten äitiys henkilökohtaisella tasolla koetaan. Äitiyden kulttuurista määrittelyä analysoidaan kahdesta tekstiaineistosta. Yhtenä aineistona ovat Stakesissa vuonna 1999 toteutetun Perhetyöprojektin yhteydessä kerätyt, lastensuojelussa toimivien perhetyöammattilaisten ryhmäkeskustelut. Toisena aineistona on projektin ajankohtana ilmestyneistä suomalaisista naisten- ja perhelehdistä (Kotiliesi, Anna, Kaksplus) kerätyt äitien haastattelut. Tutkimuksessa kysytään 1) Mihin ammattilaisten äitejä koskeva huolipuhe kiinnittyy ja millaisia kulttuurisia äitiyden odotuksia se konstruoi? 2) Millaisia äitiyden odotuksia median äitihaastattelut konstruoivat? 3) Millaisen äitiyden odotushorisontin nämä puhekäytännöt yhdessä tuottavat? Analyysin teoreettis-metodologisina kulmakivinä ovat sosiaalinen konstruktionismi ja feministinen tietokäsitys. Analyysimenetelmänä on laadullinen, aineistojen ehdoilla etenevä, feministisesti ja kriittisesti sävyttynyt lukutapa, joka hyödyntää teemoittelun, diskurssianalyysin ja feministisen metodologian ideoita ja käsitteitä. Analysoitavana olevissa keskusteluissa äitiyttä konstruoidaan lapsen tarpeiden (ammattilaiset) ja naisen tarpeiden (media) näkökulmista. Ammattilaiset puhuvat tilanteista, joissa äitien toiminta rikkoo kulttuurista hyvän äidin kuvaa, vaarantaa lapsen hyvinvointia ja äitiyteen joudutaan puuttumaan ammatillisesti. Ammattilaisten tulkinnat kuvaavat taitavaa lapsen edun näkökulmasta tehtyä arviointia, jonka kiintopisteenä ovat äidit yksilöllisine ominaisuuksineen ja piirteineen. Ammatillisen huolipuheen keskiössä ovat äidin vuorovaikutussuhteet sekä äidin tunteet, käyttäytyminen ja asenteet. Riittävää äitiyttä konstruoi kodin luominen, kiintymyssuhteen rakentaminen ja lapsen ensisijaiseksi asettaminen. Sen sijaan vaikuttaa siltä, ettei äitiyden arviointia juurikaan tehdä suhteessa äidin muihin identiteetteihin tai äitiyden toteuttamisen kontekstiin. Paikoin ammattilaisten tulkinnat heijastavat myös stereotyyppisiä ja idealistisia odotuksia, joita vasten äitiyttä arvioidaan. Tällaiset piirteet voivat kertoa siitä, että äitien avuntarpeet jäävät lastensuojelutyössä kohtaamatta ja ymmärtämättä. Mediapuhe äitiydestä käydään naiseuden ja äitiyden mallien antamisen kontekstissa. Puheen keskiössä ovat mediajulkisuuteen päässeiden naisten äidiksi tuloon ja äitiyden toteuttamiseen liittyvät valinnat ja käyttäytyminen. Mediapuhe on puhetta kulttuuristen ja ammatillisten äitiyden odotusten rikkomisesta, uudelleen tulkinnasta ja niiden muovaamisesta itselle sopiviksi. Mediapuheessa hyvää äitiyttä konstruoi äidin itsenäisyys ja oma aika, sosiaalisen elämän rikkaus, ammatillinen identiteetti ja persoonalliset valinnat. Aineistojen kautta rakentuu moninaisten ja ristiriitaisten, äitejä eri suuntaan vetävien kulttuuristen odotusten kirjo. Odotukset jäsentyvät neljälle ulottuvuudelle: 1) lapselle omistautuva – itseään toteuttava, 2) emotionaalinen side – rationaalinen tehtävä, 3) odotuksia toteuttava – omaehtoinen, 4) itsenäinen - äitiyttä jakava. Äitiyden toteuttaminen kulttuurisesti ”oikein” on näiden odotusten välissä tasapainoilua. Ulottuvuuksien kautta esille tulevat kaksoisviestit voivat heikentää äitien itsetuntoa, tuottaa riittämättömyyden tunteita tai yllyttää suorittamaan äitiyttä. Myös äitiyden ammatillinen tukeminen edellyttää tasapainoilua, jottei äitejä idealisoida tai syyllistetä kulttuurisia odotuksia vasten.