Transparent Network Interfaces to Mobile Peer-to-Peer Environment

The fact that most of new Personal Data Assistant (PDA) devices and smartphones have the ability to communicate via different wireless technologies has made several new applications possible. While traditional network model is based on the idea of static hosts, mobile devices can create decentralized, self-organizing ad-hoc networks and act as peers in the network. This kind of adapting network is suitable for mobile devices which can freely join and leave the networks. Because several different wireless communication technologies are involved, flexible changing of the networking technology must be handled in order to enable seamless communication between these networks. This thesis presents a transparent network interface to mobile Peer-to-Peer environment which is named as Virtual PeerHood. Different wireless technologies and aspects of providing a seamless connectivity between these technologies are explored. The result is a middleware platform for mobile Peer-to-Peer environment, capable of handling several networking technologies.

Moottoripolttoaineiden ominaisuuksien mallintaminen

Työn tavoitteena on selvittää voidaanko neuroverkkoa käyttää mallintamaan ja ennustamaan polttoaineen vaikutusta nykyaikaisen auton päästöihin. Näin pystyttäisiin vähentämään aikaa vievien ja kalliiden koeajojen tarvetta. Työ tehtiin Lappeenrannan teknillisen yliopiston ja Fortum Oy:n yhteistyöprojektissa. Työssä tehtiin kolme erilaista mallia. Ensimmäisenä tehtiin autokohtainen malli, jolla pyrittiin ennustamaan autokohtaista käyttäytymistä. Toiseksi kokeiltiin mallia, jossa automalli oli yhtenä syötteenä. Kolmantena yritettiin kiertää eräitä aineiston ongelmia käyttämällä "sumeutettuja" polttoaineiden koostumuksia. Työssä käytettiin MLP-neuroverkkoa, joka opetettiin backpropagation algoritmilla. Työssä havaittiin ettei käytettävissä olleella aineistolla ja käytetyillä malleilla pystytä riittävällä tarkkuudella mallintamaan polttoaineen vaikutusta päästöihin. Aineiston ongelmia olivat mm. suuret mittausvarianssit, aineiston pieni määrä sekä aineiston soveltumattomuus neuroverkolla mallintamiseen.

Local Services in a Fourth Generation Mobile Network

Neljännen sukupolven mobiiliverkot kokoaa kaikki tietoliikenneverkot ja palvelut Internetin ympärille. Tämä mullistus muuttaa vanhat vertikaaliset tietoliikenneverkot joissa yhden tietoliikenneverkon palvelut ovat saatavissa vain kyseisen verkon päätelaitteille horisontaaliseksi malliksi jossa päätelaitteet käyttävät omaa verkkoansa pääsynä Internetin palveluihin. Tämä diplomityö esittelee idean paikallisista palveluista neljännen sukupolven mobiiliverkossa. Neljännen sukupolven mobiiliverkko yhdistää perinteiset televerkkojen palvelut ja Internet palvelut sekä mahdollistaa uuden tyyppisten palveluiden luonnin. TCP/IP protokollien ja Internetin evoluutio on esitelty. Laajakaistaiset, lyhyen kantaman radiotekniikat joita käytetään langattomana yhteytenä Internetiin on käsitelty. Evoluutio kohti neljännen sukupolven mobiiliverkkoja on kuvattu esittelemällä vanhat, nykyiset ja tulevat mobiiliverkot sekä niiden palvelut. Ennustukset palveluiden ja markkinoiden tulevaisuuden kehityksestä on käsitelty. Neljännen sukupolven mobiiliverkon arkkitehtuuri mahdollistaa paikalliset palvelut jotka ovat saatavilla vain yhdessä paikallisessa 4G verkossa. Paikalliset palvelut voidaan muunnella jokaiselle käyttäjälle erikseen käyttäen profiili-informaatiota ja paikkatietoa. Työssä on pohdittu paikallisten palveluiden käyttökelpoisuutta ja mahdollisuuksia käyttäen Lappeenrannan teknillisen korkeakoulun 4G projektin palvelupilotin tuloksia.

A spatial modelling framework for assessing climate change impacts on freshwater ecosystems: Response of brown trout (Salmo trutta L.) biomass to warming water temperature

Mountain regions worldwide are particularly sensitive to on-going climate change. Specifically in the Alps in Switzerland, the temperature has increased twice as fast than in the rest of the Northern hemisphere. Water temperature closely follows the annual air temperature cycle, severely impacting streams and freshwater ecosystems. In the last 20 years, brown trout (Salmo trutta L) catch has declined by approximately 40-50% in many rivers in Switzerland. Increasing water temperature has been suggested as one of the most likely cause of this decline. Temperature has a direct effect on trout population dynamics through developmental and disease control but can also indirectly impact dynamics via food-web interactions such as resource availability. We developed a spatially explicit modelling framework that allows spatial and temporal projections of trout biomass using the Aare river catchment as a model system, in order to assess the spatial and seasonal patterns of trout biomass variation. Given that biomass has a seasonal variation depending on trout life history stage, we developed seasonal biomass variation models for three periods of the year (Autumn-Winter, Spring and Summer). Because stream water temperature is a critical parameter for brown trout development, we first calibrated a model to predict water temperature as a function of air temperature to be able to further apply climate change scenarios. We then built a model of trout biomass variation by linking water temperature to trout biomass measurements collected by electro-fishing in 21 stations from 2009 to 2011. The different modelling components of our framework had overall a good predictive ability and we could show a seasonal effect of water temperature affecting trout biomass variation. Our statistical framework uses a minimum set of input variables that make it easily transferable to other study areas or fish species but could be improved by including effects of the biotic environment and the evolution of demographical parameters over time. However, our framework still remains informative to spatially highlight where potential changes of water temperature could affect trout biomass. (C) 2015 Elsevier B.V. All rights reserved.-

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

Sleep in vitro : Erk pathway regulates sleep duration and sleep related genes

To date, for most biological and physiological phenomena, the scientific community has reach a consensus on their related function, except for sleep, which has an undetermined, albeit mystery, function. To further our understanding of sleep function(s), we first focused on the level of complexity at which sleep-like phenomenon can be observed. This lead to the development of an in vitro model. The second approach was to understand the molecular and cellular pathways regulating sleep and wakefulness, using both our in vitro and in vivo models. The third approach (ongoing) is to look across evolution when sleep or wakefulness appears. (1) To address the question as to whether sleep is a cellular property and how this is linked to the entire brain functioning, we developed a model of sleep in vitro by using dissociated primary cortical cultures. We aimed at simulating the major characteristics of sleep and wakefulness in vitro. We have shown that mature cortical cultures display a spontaneous electrical activity similar to sleep. When these cultures are stimulated by waking neurotransmitters, they show a tonic firing activity, similar to wakefulness, but return spontaneously to the "sleep-like" state 24h after stimulation. We have also shown that transcriptional, electrophysiological, and metabolic correlates of sleep and wakefulness can be reliably detected in dissociated cortical cultures. (2) To further understand at which molecular and cellular levels changes between sleep and wakefulness occur, we have used a pharmacological and systematic gene transcription approach in vitro and discovered a major role played by the Erk pathway. Indeed, pharmacological inhibition of this pathway in living animals decreased sleep by 2 hours per day and consolidated both sleep and wakefulness by reducing their fragmentation. (3) Finally, we tried to evaluate the presence of sleep in one of the most primitive species with a neural network. We set up Hydra as a model organism. We hypothesized that sleep as a cellular (neuronal) property may occur with the appearance of the most primitive nervous system. We were able to show that Hydra have periodic rest phases amounting to up to 5 hours per day. In conclusion, our work established an in vitro model to study sleep, discovered one of the major signaling pathways regulating vigilance states, and strongly suggests that sleep is a cellular property highly conserved at the molecular level during evolution. -- Jusqu'à ce jour, la communauté scientifique s'est mise d'accord sur la fonction d'une majorité des processus physiologiques, excepté pour le sommeil. En effet, la fonction du sommeil reste un mystère, et aucun consensus n'est atteint le concernant. Pour mieux comprendre la ou les fonctions du sommeil, (1) nous nous sommes d'abord concentré sur le niveau de complexité auquel un état ressemblant au sommeil peut être observé. Nous avons ainsi développé un modèle du sommeil in vitro, (2) nous avons disséqué les mécanismes moléculaires et cellulaires qui pourraient réguler le sommeil, (3) nous avons cherché à savoir si un état de sommeil peut être trouvé dans l'hydre, l'animal le plus primitif avec un système nerveux. (1) Pour répondre à la question de savoir à quel niveau de complexité apparaît un état de sommeil ou d'éveil, nous avons développé un modèle du sommeil, en utilisant des cellules dissociées de cortex. Nous avons essayé de reproduire les corrélats du sommeil et de l'éveil in vitro. Pour ce faire, nous avons développé des cultures qui montrent les signes électrophysiologiques du sommeil, puis quand stimulées chimiquement passent à un état proche de l'éveil et retournent dans un état de sommeil 24 heures après la stimulation. Notre modèle n'est pas parfait, mais nous avons montré que nous pouvions obtenir les corrélats électrophysiologiques, transcriptionnels et métaboliques du sommeil dans des cellules corticales dissociées. (2) Pour mieux comprendre ce qui se passe au niveau moléculaire et cellulaire durant les différents états de vigilance, nous avons utilisé ce modèle in vitro pour disséquer les différentes voies de signalisation moléculaire. Nous avons donc bloqué pharmacologiquement les voies majeures. Nous avons mis en évidence la voie Erkl/2 qui joue un rôle majeur dans la régulation du sommeil et dans la transcription des gènes qui corrèlent avec le cycle veille-sommeil. En effet, l'inhibition pharmacologique de cette voie chez la souris diminue de 2 heures la quantité du sommeil journalier et consolide l'éveil et le sommeil en diminuant leur fragmentation. (3) Finalement, nous avons cherché la présence du sommeil chez l'Hydre. Pour cela, nous avons étudié le comportement de l'Hydre pendant 24-48h et montrons que des périodes d'inactivité, semblable au sommeil, sont présentes dans cette espèce primitive. L'ensemble de ces travaux indique que le sommeil est une propriété cellulaire, présent chez tout animal avec un système nerveux et régulé par une voie de signalisation phylogénétiquement conservée.

Neural mechanisms underlying adaptive actions after slips

An increase in cognitive control has been systematically observed in responses produced immediately after the commission of an error. Such responses show a delay in reaction time (post-error slowing) and an increase in accuracy. To characterize the neurophysiological mechanism involved in the adaptation of cognitive control, we examined oscillatory electrical brain activity by electroencephalogram and its corresponding neural network by event-related functional magnetic resonance imaging in three experiments. We identified a new oscillatory thetabeta component related to the degree of post-error slowing in the correct responses following an erroneous trial. Additionally, we found that the activity of the right dorsolateral prefrontal cortex, the right inferior frontal cortex, and the right superior frontal cortex was correlated with the degree of caution shown in the trial following the commission of an error. Given the overlap between this brain network and the regions activated by the need to inhibit motor responses in a stop-signal manipulation, we conclude that the increase in cognitive control observed after the commission of an error is implemented through the participation of an inhibitory mechanism.

Determinação de constituintes químicos em madeira de eucalipto por Pi-CG/EM e calibração multivariada: comparação entre redes neurais artificiais e máquinas de vetor suporte

Multivariate models were developed using Artificial Neural Network (ANN) and Least Square - Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by pyrolysis associated to gaseous chromatography and mass spectrometry (Py-GC/MS). The results obtained by two calibration methods were in agreement with those of reference methods. However a comparison indicated that the LS-SVM model presented better predictive capacity for the cellulose and lignin contents, while the ANN model presented was more adequate for estimating the hemicelluloses content and lignin siringyl/guaiacyl ratio.

Methane combustion kinetic rate constants determination: an ill-posed inverse problem analysis

Methane combustion was studied by the Westbrook and Dryer model. This well-established simplified mechanism is very useful in combustion science, for computational effort can be notably reduced. In the inversion procedure to be studied, rate constants are obtained from [CO] concentration data. However, when inherent experimental errors in chemical concentrations are considered, an ill-conditioned inverse problem must be solved for which appropriate mathematical algorithms are needed. A recurrent neural network was chosen due to its numerical stability and robustness. The proposed methodology was compared against Simplex and Levenberg-Marquardt, the most used methods for optimization problems.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Phase voltage control and filtering in a converter-fed single-phase customer-end system of the LVDC distribution network

In recent years, the network vulnerability to natural hazards has been noticed. Moreover, operating on the limits of the network transmission capabilities have resulted in major outages during the past decade. One of the reasons for operating on these limits is that the network has become outdated. Therefore, new technical solutions are studied that could provide more reliable and more energy efficient power distributionand also a better profitability for the network owner. It is the development and price of power electronics that have made the DC distribution an attractive alternative again. In this doctoral thesis, one type of a low-voltage DC distribution system is investigated. Morespecifically, it is studied which current technological solutions, used at the customer-end, could provide better power quality for the customer when compared with the current system. To study the effect of a DC network on the customer-end power quality, a bipolar DC network model is derived. The model can also be used to identify the supply parameters when the V/kW ratio is approximately known. Although the model provides knowledge of the average behavior, it is shown that the instantaneous DC voltage ripple should be limited. The guidelines to choose an appropriate capacitance value for the capacitor located at the input DC terminals of the customer-end are given. Also the structure of the customer-end is considered. A comparison between the most common solutions is made based on their cost, energy efficiency, and reliability. In the comparison, special attention is paid to the passive filtering solutions since the filter is considered a crucial element when the lifetime expenses are determined. It is found out that the filter topology most commonly used today, namely the LC filter, does not provide economical advantage over the hybrid filter structure. Finally, some of the typical control system solutions are introduced and their shortcomings are presented. As a solution to the customer-end voltage regulation problem, an observer-based control scheme is proposed. It is shown how different control system structures affect the performance. The performance meeting the requirements is achieved by using only one output measurement, when operating in a rigid network. Similar performance can be achieved in a weak grid by DC voltage measurement. An additional improvement can be achieved when an adaptive gain scheduling-based control is introduced. As a conclusion, the final power quality is determined by a sum of various factors, and the thesis provides the guidelines for designing the system that improves the power quality experienced by the customer.

Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

Stiffness based trajectory planning and feedforward based vibration suppression control of parallel robot machines

The dissertation proposes two control strategies, which include the trajectory planning and vibration suppression, for a kinematic redundant serial-parallel robot machine, with the aim of attaining the satisfactory machining performance. For a given prescribed trajectory of the robot's end-effector in the Cartesian space, a set of trajectories in the robot's joint space are generated based on the best stiffness performance of the robot along the prescribed trajectory. To construct the required system-wide analytical stiffness model for the serial-parallel robot machine, a variant of the virtual joint method (VJM) is proposed in the dissertation. The modified method is an evolution of Gosselin's lumped model that can account for the deformations of a flexible link in more directions. The effectiveness of this VJM variant is validated by comparing the computed stiffness results of a flexible link with the those of a matrix structural analysis (MSA) method. The comparison shows that the numerical results from both methods on an individual flexible beam are almost identical, which, in some sense, provides mutual validation. The most prominent advantage of the presented VJM variant compared with the MSA method is that it can be applied in a flexible structure system with complicated kinematics formed in terms of flexible serial links and joints. Moreover, by combining the VJM variant and the virtual work principle, a systemwide analytical stiffness model can be easily obtained for mechanisms with both serial kinematics and parallel kinematics. In the dissertation, a system-wide stiffness model of a kinematic redundant serial-parallel robot machine is constructed based on integration of the VJM variant and the virtual work principle. Numerical results of its stiffness performance are reported. For a kinematic redundant robot, to generate a set of feasible joints' trajectories for a prescribed trajectory of its end-effector, its system-wide stiffness performance is taken as the constraint in the joints trajectory planning in the dissertation. For a prescribed location of the end-effector, the robot permits an infinite number of inverse solutions, which consequently yields infinite kinds of stiffness performance. Therefore, a differential evolution (DE) algorithm in which the positions of redundant joints in the kinematics are taken as input variables was employed to search for the best stiffness performance of the robot. Numerical results of the generated joint trajectories are given for a kinematic redundant serial-parallel robot machine, IWR (Intersector Welding/Cutting Robot), when a particular trajectory of its end-effector has been prescribed. The numerical results show that the joint trajectories generated based on the stiffness optimization are feasible for realization in the control system since they are acceptably smooth. The results imply that the stiffness performance of the robot machine deviates smoothly with respect to the kinematic configuration in the adjacent domain of its best stiffness performance. To suppress the vibration of the robot machine due to varying cutting force during the machining process, this dissertation proposed a feedforward control strategy, which is constructed based on the derived inverse dynamics model of target system. The effectiveness of applying such a feedforward control in the vibration suppression has been validated in a parallel manipulator in the software environment. The experimental study of such a feedforward control has also been included in the dissertation. The difficulties of modelling the actual system due to the unknown components in its dynamics is noticed. As a solution, a back propagation (BP) neural network is proposed for identification of the unknown components of the dynamics model of the target system. To train such a BP neural network, a modified Levenberg-Marquardt algorithm that can utilize an experimental input-output data set of the entire dynamic system is introduced in the dissertation. Validation of the BP neural network and the modified Levenberg- Marquardt algorithm is done, respectively, by a sinusoidal output approximation, a second order system parameters estimation, and a friction model estimation of a parallel manipulator, which represent three different application aspects of this method.

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.