Improving plant functional groups for dynamic models of biodiversity: at the crossroads between functional and community ecology

The pace of on-going climate change calls for reliable plant biodiversity scenarios. Traditional dynamic vegetation models use plant functional types that are summarized to such an extent that they become meaningless for biodiversity scenarios. Hybrid dynamic vegetation models of intermediate complexity (hybrid-DVMs) have recently been developed to address this issue. These models, at the crossroads between phenomenological and process-based models, are able to involve an intermediate number of well-chosen plant functional groups (PFGs). The challenge is to build meaningful PFGs that are representative of plant biodiversity, and consistent with the parameters and processes of hybrid-DVMs. Here, we propose and test a framework based on few selected traits to define a limited number of PFGs, which are both representative of the diversity (functional and taxonomic) of the flora in the Ecrins National Park, and adapted to hybrid-DVMs. This new classification scheme, together with recent advances in vegetation modeling, constitutes a step forward for mechanistic biodiversity modeling.

A benchmark test for a quantitative assessment of simple neuron models

Several methods and algorithms have recently been proposed that allow for the systematic evaluation of simple neuron models from intracellular or extracellular recordings. Models built in this way generate good quantitative predictions of the future activity of neurons under temporally structured current injection. It is, however, difficult to compare the advantages of various models and algorithms since each model is designed for a different set of data. Here, we report about one of the first attempts to establish a benchmark test that permits a systematic comparison of methods and performances in predicting the activity of rat cortical pyramidal neurons. We present early submissions to the benchmark test and discuss implications for the design of future tests and simple neurons models

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Predictive Value of Readiness, Importance, and Confidence in Ability to Change Drinking and Smoking

Background: Visual analog scales (VAS) are used to assess readiness to changeconstructs, which are often considered critical for change.Objective: We studied whether 3 constructs -readiness to change, importance of changing and confidence inability to change- predict risk status 6 months later in 20 year-old men with either orboth of two behaviors: risky drinking and smoking. Methods: 577 participants in abrief intervention randomized trial were assessed at baseline and 6 months later onalcohol and tobacco consumption and with three 1-10 VAS (readiness, importance,confidence) for each behavior. For each behavior, we used one regression model foreach constructs. Models controlled for receipt of a brief intervention and used thelowest level (1-4) in each construct as the reference group (vs medium (5-7) and high(8-10) levels).Results: Among the 475 risky drinkers, mean (SD) readiness, importance and confidence to change drinking were 4.0 (3.1), 2.8 (2.2) and 7.2 (3.0).Readiness was not associated with being alcohol-risk free 6 months later (OR 1.3[0.7; 2.2] and 1.4 [0.8; 2.6] for medium and high readiness). High importance andhigh confidence were associated with being risk free (OR 0.9 [0.5; 1.8] and 2.9 [1.2;7.5] for medium and high importance; 2.1 [1.0;4.8] and 2.8 [1.5;5.6] for medium andhigh confidence). Among the 320 smokers, mean readiness, importance andconfidence to change smoking were 4.6 (2.6), 5.3 (2.6) and 5.9 (2.6). Neitherreadiness nor importance were associated with being smoking free (OR 2.1 [0.9; 4.7]and 2.1 [0.8; 5.8] for medium and high readiness; 1.4 [0.6; 3.4] and 2.1 [0.8; 5.4] formedium and high importance). High confidence was associated with being smokingfree (OR 2.2 [0.8;6.6] and 3.4 [1.2;9.8] for medium and high confidence).Conclusions: For drinking and smoking, high confidence in ability to change wasassociated -with similar magnitude- with a favorable outcome. This points to thevalue of confidence as an important predictor of successful change.

Longer term clinical and virological outcome of sub-Saharan African participants on antiretroviral treatment in the Swiss HIV Cohort Study.

OBJECTIVES: Persons from sub-Saharan Africa (SSA) are increasingly enrolled in the Swiss HIV Cohort Study (SHCS). Cohorts from other European countries showed higher rates of viral failure among their SSA participants. We analyzed long-term outcomes of SSA versus North Western European participants. DESIGN: We analyzed data of the SHCS, a nation-wide prospective cohort study of HIV-infected adults at 7 sites in Switzerland. METHODS: SSA and North Western European participants were included if their first treatment combination consisted of at least 3 antiretroviral drugs (cART), if they had at least 1 follow-up visit, did not report active injecting drug use, and did not start cART with CD4 counts >200 cells per microliter during pregnancy. Early viral response, CD4 cell recovery, viral failure, adherence, discontinuation from SHCS, new AIDS-defining events, and survival were analyzed using linear regression and Cox proportional hazard models. RESULTS: The proportion of participants from SSA within the SHCS increased from 2.6% (<1995) to 20.8% (2005-2009). Of 4656 included participants, 808 (17.4%) were from SSA. Early viral response (6 months) and rate of viral failure in an intent-to-stay-on-cART approach were similar. However, SSA participants had a higher risk of viral failure on cART (adjusted hazard ratio: 2.03, 95% confidence interval: 1.50 to 2.75). Self-reported adherence was inferior for SSA. There was no increase of AIDS-defining events or mortality in SSA participants. CONCLUSIONS: Increased attention must be given to factors negatively influencing adherence to cART in participants from SSA to guarantee equal longer-term results on cART.

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed

Geometric stopping of a random walk and its applications to valuing equity-linked death benefits

We study discrete-time models in which death benefits can depend on a stock price index, the logarithm of which is modeled as a random walk. Examples of such benefit payments include put and call options, barrier options, and lookback options. Because the distribution of the curtate-future-lifetime can be approximated by a linear combination of geometric distributions, it suffices to consider curtate-future-lifetimes with a geometric distribution. In binomial and trinomial tree models, closed-form expressions for the expectations of the discounted benefit payment are obtained for a series of options. They are based on results concerning geometric stopping of a random walk, in particular also on a version of the Wiener-Hopf factorization.

The Future of Mobile Services and Wireless Applications in the Finnish Healthcare Sector

Työn päätavoitteena oli tutkia mobiilipalveluita ja langattomia sovelluksia Suomen terveydenhuollon sektorilla. Tutkimus havainnollistaa avain-alueita, missä mobiilipalvelut ja langattomat sovellukset voivat antaa lisäarvoa perinteiseen lääketieteen harjoittamiseen, ja selvittää, mitkä ovat tähän kehitykseen liittyvät suurimmat ongelmat ja uhkat sekä tutkimustuloksiin pohjautuvat mahdolliset palvelut ja sovellukset 5-10 vuoden kuluttua. Tutkimus oli luonteeltaan kvalitatiivinen ja tutkimuksen toteuttamiseen valittiin tulevaisuudentutkimus ja erityisesti yksi sen menetelmistä, delfoi-menetelmä. Tutkimuksen aineisto kerättiin kahdelta puolistrukturoidulta haastattelukierrokselta. Työn empiirinen osuus keskittyi kuvailemaan Suomen terveydenhuollon sektoria, siinä meneillään olevia projekteja sekä teknisiä esteitä. Lisäksi pyrittiin vastaamaan tutkimuksen pääkysymykseen. Tutkimustulokset osoittivat, että tärkeät alueet, joihin langaton kommunikaatio tulisi vaikuttamaan merkittävästi, ovat ensiaputoiminta, kroonisten potilaiden etämonitorointi, välineiden kehittäminen langattomaan kommunikaatioon kotihoidon parantamiseksi ja uusien toimintamallien luomiseksi sekä lääketieteellinen yhteistyö jakamalla terveydenhuoltoon liittyvät informaation lähteet. Työn tulosten perusteellavoitiin antaa myös muutamia toimenpide-ehdotuksia jatkotutkimuksia varten.

Development of e-Service tools for customer services and processes, Case: KONE Oyj Finland

Tutkimuksen tavoitteena oli selvittää miten kehittää yrityksen nykyistä e-palvelujärjestelmää, Internet -teknologiaan perustuvaa sähköisiä kommunikaatio- ja tiedonjakojärjestelmää, yrityksen business-to-business asiakkuuksien johtamisessa. Tavoitteena oli myös luoda ehdotukset uusista e-palvelusopimusmalleista. Tutkimuksen teoriaosuudessa pyrittiin kehittämään aikaisempiin tutkimuksiin, tietokirjallisuuteen ja asiantuntijoihin perustuva viitekehysmalli. Empiirisessä osassa tutkimuksen tavoitteisiin pyrittiin haastattelemalla yrityksen asiakkaita ja henkilöstöä, sekä tarkastelemalla asiakaskontaktien nykyistä tilaa ja kehittymistä. Näiden tietojen perusteella selvitettiin e-palvelun käyttäjien tarpeita, profiilia ja valmiuksia palvelun käyttöön sekä palvelun nykyistä houkuttelevuutta. Tutkimuksen teoriaosan lähdeaineistona käytettiin kirjallisuutta, artikkeleita ja tilastoja asiakashallinnasta sekä e-palveluiden, erityisesti Internet ja verkkopalveluiden markkinoinnista, nykytilasta sekä palveluiden kehittämisestä. Lisäksi tutkittiin kirjallisuutta arvoverkostoanalyysistä, asiakkaan arvosta, informaatioteknologiasta, palvelun laadusta ja asiakastyytyväisyydestä. Tutkimuksen empiirinen osa perustuu yrityksen henkilöstöltä sekä asiakkailta haastatteluissa kerättyihin tietoihin, yrityksen ennalta keräämiin materiaaleihin sekä Taloustutkimuksen keräämiin tietoihin. Tutkimuksessa käytettiin case -menetelmää, joka oli yhdistelmä sekä kvalitatiivista että kvantitatiivista tutkimusta. Casen tarkoituksena oli testata mallin paikkansapitävyyttä ja käyttökelpoisuutta, sekä selvittää onko olemassa vielä muita tekijöitä, jotka vaikuttavat asiakkaan saamaan arvoon. Kvalitatiivinen aineisto perustuu teemahaastattelumenetelmää soveltaen haastateltuihin asiakkaisiin ja yrityksen työntekijöihin. Kvantitatiivinen tutkimus perustuu Taloustutkimuksen tutkimukseen ja yrityksen asiakaskontakteista kerättyyn tietoon. Haastatteluiden perusteella e-palvelut nähtiin hyödyllisinä ja tulevaisuudessa erittäin tärkeinä. E-palvelut nähdään yhtenä tärkeänä kanavana, perinteisten kanavien rinnalla, tehostaa business-to-business -asiakkuuksien johtamista. Tutkimuksen antamien tulosten mukaan asiakkaiden palveluun liittyvän tieto-, taito-, tarpeellisuus- ja kiinnostavuustasojen vaihtelevaisuus osoittaa selvän tarpeen eritasoisille e-palvelupaketti ratkaisuille. Tuloksista muodostettu ratkaisuehdotus käsittää neljän eri e-palvelupaketin rakentamisen asiakkaiden eri tarpeita mukaillen.

Differential associations of personal and general just-world beliefs with the Five-Factor and HEXACO models of personality.

Recent literature evidences differential associations of personal and general just-world beliefs with constructs in the interpersonal domain. In line with this research, we examine the respective relationships of each just-world belief with the Five-Factor and the HEXACO models of personality in one representative sample of the working population of Switzerland and one sample of the general US population, respectively. One suppressor effect was observed in both samples: Neuroticism and emotionality was positively associated with general just-world belief, but only after controlling for personal just-world belief. In addition, agreeableness was positively and honesty-humility negatively associated with general just-world belief but unrelated to personal just-world belief. Conscientiousness was consistently unrelated to any of the just-world belief and extraversion and openness to experience revealed unstable coefficients across studies. We discuss these points in light of just-world theory and their implications for future research taking both dimensions into account.

Studying the recent situation of information and communication technology industry

Globaalinen liiketoimintaympäristö on muutoksessa. Uudet teknologiat muuttavat toimintaympäristöä ja talouden säännöt muuttuvat nopeasti. Uusia liiketoimintamalleja tarvitaan. Tutkimuksen tavoitteena oli analysoida tieto- ja viestintäteollisuuden (ICT-teollisuus) nykytilannetta strategisesta ja kilpailuanalyyttisestä näkökulmasta, sekä luoda kuva ICT-teollisuudesta ja sen suurista pelureista Euroopassa ja USA:ssa. Tutkimus analysoi viittä suurta ICT-alan yritystä. Tutkimus oli luonteeltaan sekä kvalitatiivinen että kvantitatiivinen. Yrityksiä analysoitiin käyttäen numeerista ja laadullista materiaalia. Tutkimus perustui kirjallisuuteen, artikkeleihin, tutkimusraportteihin, yritysten internet-kotisivuihin ja vuosikertomuksiin. Tutkimuksen tuloksena voitiin löytää sekä yhtäläisyyksiä että eroavaisuuksia yritysten liiketoimintamallien ja taloudellisen menestymisen väliltä.

Estimating 750 years of temperature variations and uncertainties in the Pyrenees by tree-ring reconstructions and climate simulations.

Past temperature variations are usually inferred from proxy data or estimated using general circulation models. Comparisons between climate estimations derived from proxy records and from model simulations help to better understand mechanisms driving climate variations, and also offer the possibility to identify deficiencies in both approaches. This paper presents regional temperature reconstructions based on tree-ring maximum density series in the Pyrenees, and compares them with the output of global simulations for this region and with regional climate model simulations conducted for the target region. An ensemble of 24 reconstructions of May-to-September regional mean temperature was derived from 22 maximum density tree-ring site chronologies distributed over the larger Pyrenees area. Four different tree-ring series standardization procedures were applied, combining two detrending methods: 300-yr spline and the regional curve standardization (RCS). Additionally, different methodological variants for the regional chronology were generated by using three different aggregation methods. Calibration verification trials were performed in split periods and using two methods: regression and a simple variance matching. The resulting set of temperature reconstructions was compared with climate simulations performed with global (ECHO-G) and regional (MM5) climate models. The 24 variants of May-to-September temperature reconstructions reveal a generally coherent pattern of inter-annual to multi-centennial temperature variations in the Pyrenees region for the last 750 yr. However, some reconstructions display a marked positive trend for the entire length of the reconstruction, pointing out that the application of the RCS method to a suboptimal set of samples may lead to unreliable results. Climate model simulations agree with the tree-ring based reconstructions at multi-decadal time scales, suggesting solar variability and volcanism as the main factors controlling preindustrial mean temperature variations in the Pyrenees. Nevertheless, the comparison also highlights differences with the reconstructions, mainly in the amplitude of past temperature variations and in the 20th century trends. Neither proxy-based reconstructions nor model simulations are able to perfectly track the temperature variations of the instrumental record, suggesting that both approximations still need further improvements.

Business models enhancing the diffusion of mobile Internet in emerging markets

Tämän tutkimuksen päätavoitteena oli selvittää, millaiset liiketoimintamallit soveltuvat mobiilin internet-liiketoiminnan harjoittamiseen kehittyvillä markkinoilla. Tavoitteena oli myös selvittää tekijöitä, jotka vaikuttavat mobiilin internetin diffuusioon. Tutkimus tehtiin käyttäen sekä kvantitatiivista että kvalitatiivista tutkimusmenetelmää. Klusterianalyysin avulla 40 Euroopan maasta muodostettiin sisäisesti homogeenisiä maaklustereita. Näiden klustereiden avulla oli mahdollista suunnitella erityyppisille markkinoille soveltuvat liiketoimintamallit. Haastatteluissa selvitettiin asiantuntijoiden näkemyksiä tekijöistä, jotka vaikuttavat mobiilin internetin diffuusioon kehittyvillä markkinoilla. Tutkimuksessa saatiin selville, että tärkeimmät liiketoimintamallin elementit kehittyvillä markkinoilla ovat hinnoittelu, arvotarjooma ja arvoverkko. Puutteellisen kiinteän verkon todettiin olevan yksi tärkeimmistä mobiilin internetin diffuusiota edistävistä tekijöistä kehittyvillä markkinoilla.