Multivariate analysis and geostatistics of the fertility of a humic rhodic hapludox under coffee cultivation

The spatial variability of soil and plant properties exerts great influence on the yeld of agricultural crops. This study analyzed the spatial variability of the fertility of a Humic Rhodic Hapludox with Arabic coffee, using principal component analysis, cluster analysis and geostatistics in combination. The experiment was carried out in an area under Coffea arabica L., variety Catucai 20/15 - 479. The soil was sampled at a depth 0.20 m, at 50 points of a sampling grid. The following chemical properties were determined: P, K+, Ca2+, Mg2+, Na+, S, Al3+, pH, H + Al, SB, t, T, V, m, OM, Na saturation index (SSI), remaining phosphorus (P-rem), and micronutrients (Zn, Fe, Mn, Cu and B). The data were analyzed with descriptive statistics, followed by principal component and cluster analyses. Geostatistics were used to check and quantify the degree of spatial dependence of properties, represented by principal components. The principal component analysis allowed a dimensional reduction of the problem, providing interpretable components, with little information loss. Despite the characteristic information loss of principal component analysis, the combination of this technique with geostatistical analysis was efficient for the quantification and determination of the structure of spatial dependence of soil fertility. In general, the availability of soil mineral nutrients was low and the levels of acidity and exchangeable Al were high.

SF 2088 Convenience Fees Analysis and Recommendations, November 16, 2011

As an expansion of SF2088, the Department of Administrative Services-Information Technology Enterprise (DAS-ITE) was asked to further analyze the potential costs and savings if the current practice of charging credit card and overhead fees (“value-added fees”) were to be eliminated. Value-added fees reflect the costs an agency incurs while providing online services, and those costs will always exist.. DAS-ITE researched these costs and identified ways of making the associated fees less burdensome to the citizens of Iowa. The three alternatives provide different ways in which agencies can recover those costs; they could be borne by either an annual appropriation or adjustment of the online service “price” to include the fees within the cost of the online transaction. An additional alternative is presented to leave the current value-added fee practices in place. Recognition must also be made of the fact that traditional forms of conducting business with the State of Iowa, face-to-face and paper-based transactions, are inherently more costly. These delivery channels are effectively subsidized by the agency as a “cost of doing business” and the associated expense of the transactions is not passed on to the customer.

Hwyneeds: Methodology, Analysis and Evaluation, March 2001

The quadrennial need study was developed to assist in identifying county highway financial needs (construction, rehabilitation, maintenance, and administration) and in the distribution of the road use tax fund (RUTF) among the counties in the state. During the period since the need study was first conducted using HWYNEEDS software, between 1982 and 1998, there have been large fluctuations in the level of funds distributed to individual counties. A recent study performed by Jim Cable (HR-363, 1993), found that one of the major factors affecting the volatility in the level of fluctuations is the quality of the pavement condition data collected and the accuracy of these data. In 1998, the Center for Transportation Research and Education researchers (Maze and Smadi) completed a project to study the feasibility of using automated pavement condition data collected for the Iowa Pavement Management Program (IPMP) for the paved county roads to be used in the HWYNEEDS software (TR-418). The automated condition data are objective and also more current since they are collected in a two year cycle compared to the 10-year cycle used by HWYNEEDS right now. The study proved the use of the automated condition data in HWYNEEDS would be feasible and beneficial in educing fluctuations when applied to a pilot study area. In another recommendation from TR-418, the researchers recommended a full analysis and investigation of HWYNEEDS methodology and parameters (for more information on the project, please review the TR-418 project report). The study reported in this document builds on the previous study on using the automated condition data in HWYNEEDS and covers the analysis and investigation of the HWYNEEDS computer program methodology and parameters. The underlying hypothesis for this study is thatalong with the IPMP automated condition data, some changes need to be made to HWYNEEDS parameters to accommodate the use of the new data, which will stabilize the process of allocating resources and reduce fluctuations from one quadrennial need study to another. Another objective of this research is to investigate the gravel roads needs and study the feasibility of developing a more objective approach to determining needs on the counties gravel road network. This study identifies new procedures by which the HWYNEEDS computer program is used to conduct the quadrennial needs study on paved roads. Also, a new procedure will be developed to determine gravel roads needs outside of the HWYNEED program. Recommendations are identified for the new procedures and also in terms of making changes to the current quadrennial need study. Future research areas are also identified.

X-ray diffraction, thermal analysis, and Raman scattering study of K2BeF4 and comparation to other member of the (beta)-K2SO4 family with ferroelectric -paraelectric transition

Thermal analysis, powder diffraction, and Raman scattering as a function of the temperature were carried out on K2BeF4. Moreover, the crystal structure was determined at 293 K from powder diffraction. The compound shows a transition from Pna21 to Pnam space group at 921 K with a transition enthalpy of 5 kJ/mol. The transition is assumed to be first order because the compound shows metastability. Structurally and spectroscopically the transition is similar to those observed in (NH4)2SO4, which suggests that the low-temperature phase is ferroelectric. In order to confirm it, the spontaneous polarization has been computed using an ionic model.

Rewarding civil service performance through team bonuses : findings, analysis and recommendations

Performance-related pay within public organizations is continuing to spread. Although it can help to strengthen an entrepreneurial spirit in civil servants, its implementation is marred by technical, financial, managerial and cultural problems. This article identifies an added problem, namely the contradiction that exists between a managerial discourse that emphasizes the team and collective performance, on the one hand, and the use of appraisal and reward tools that are above all individual, on the other. Based on an empirical survey carried out within Swiss public organizations, the analysis shows that the team is currently rarely taken into account and singles out the principal routes towards an integrated system for the management and rewarding of civil servants.

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

Detection, analysis and monitoring of slope movements by high-resolution digital elevation models

Summary Detection, analysis and monitoring of slope movements by high-resolution digital elevation modelsSlope movements, such as rockfalls, rockslides, shallow landslides or debris flows, are frequent in many mountainous areas. These natural hazards endanger the inhabitants and infrastructures making it necessary to assess the hazard and risk caused by these phenomena. This PhD thesis explores various approaches using digital elevation models (DEMs) - and particularly high-resolution DEMs created by aerial or terrestrial laser scanning (TLS) - that contribute to the assessment of slope movement hazard at regional and local scales.The regional detection of areas prone to rockfalls and large rockslides uses different morphologic criteria or geometric instability factors derived from DEMs, i.e. the steepness of the slope, the presence of discontinuities, which enable a sliding mechanism, and the denudation potential. The combination of these factors leads to a map of susceptibility to rockfall initiation that is in good agreement with field studies as shown with the example of the Little Mill Campground area (Utah, USA). Another case study in the Illgraben catchment in the Swiss Alps highlighted the link between areas with a high denudation potential and actual rockfall areas.Techniques for a detailed analysis and characterization of slope movements based on high-resolution DEMs have been developed for specific, localized sites, i.e. ancient slide scars, present active instabilities or potential slope instabilities. The analysis of the site's characteristics mainly focuses on rock slopes and includes structural analyses (orientation of discontinuities); estimation of spacing, persistence and roughness of discontinuities; failure mechanisms based on the structural setting; and volume calculations. For the volume estimation a new 3D approach was tested to reconstruct the topography before a landslide or to construct the basal failure surface of an active or potential instability. The rockslides at Åknes, Tafjord and Rundefjellet in western Norway were principally used as study sites to develop and test the different techniques.The monitoring of slope instabilities investigated in this PhD thesis is essentially based on multitemporal (or sequential) high-resolution DEMs, in particular sequential point clouds acquired by TLS. The changes in the topography due to slope movements can be detected and quantified by sequential TLS datasets, notably by shortest distance comparisons revealing the 3D slope movements over the entire region of interest. A detailed analysis of rock slope movements is based on the affine transformation between an initial and a final state of the rock mass and its decomposition into translational and rotational movements. Monitoring using TLS was very successful on the fast-moving Eiger rockslide in the Swiss Alps, but also on the active rockslides of Åknes and Nordnesfjellet (northern Norway). One of the main achievements on the Eiger and Aknes rockslides is to combine the site's morphology and structural setting with the measured slope movements to produce coherent instability models. Both case studies also highlighted a strong control of the structures in the rock mass on the sliding directions. TLS was also used to monitor slope movements in soils, such as landslides in sensitive clays in Québec (Canada), shallow landslides on river banks (Sorge River, Switzerland) and a debris flow channel (Illgraben).The PhD thesis underlines the broad uses of high-resolution DEMs and especially of TLS in the detection, analysis and monitoring of slope movements. Future studies should explore in more depth the different techniques and approaches developed and used in this PhD, improve them and better integrate the findings in current hazard assessment practices and in slope stability models.Résumé Détection, analyse et surveillance de mouvements de versant à l'aide de modèles numériques de terrain de haute résolutionDes mouvements de versant, tels que des chutes de blocs, glissements de terrain ou laves torrentielles, sont fréquents dans des régions montagneuses et mettent en danger les habitants et les infrastructures ce qui rend nécessaire d'évaluer le danger et le risque causé par ces phénomènes naturels. Ce travail de thèse explore diverses approches qui utilisent des modèles numériques de terrain (MNT) et surtout des MNT de haute résolution créés par scanner laser terrestre (SLT) ou aérien - et qui contribuent à l'évaluation du danger de mouvements de versant à l'échelle régionale et locale.La détection régionale de zones propices aux chutes de blocs ou aux éboulements utilise plusieurs critères morphologiques dérivés d'un MNT, tels que la pente, la présence de discontinuités qui permettent un mécanisme de glissement ou le potentiel de dénudation. La combinaison de ces facteurs d'instabilité mène vers une carte de susceptibilité aux chutes de blocs qui est en accord avec des travaux de terrain comme démontré avec l'exemple du Little Mill Campground (Utah, États-Unis). Un autre cas d'étude - l'Illgraben dans les Alpes valaisannes - a mis en évidence le lien entre les zones à fort potentiel de dénudation et les sources effectives de chutes de blocs et d'éboulements.Des techniques pour l'analyse et la caractérisation détaillée de mouvements de versant basées sur des MNT de haute résolution ont été développées pour des sites spécifiques et localisés, comme par exemple des cicatrices d'anciens éboulements et des instabilités actives ou potentielles. Cette analyse se focalise principalement sur des pentes rocheuses et comprend l'analyse structurale (orientation des discontinuités); l'estimation de l'espacement, la persistance et la rugosité des discontinuités; l'établissement des mécanismes de rupture; et le calcul de volumes. Pour cela une nouvelle approche a été testée en rétablissant la topographie antérieure au glissement ou en construisant la surface de rupture d'instabilités actuelles ou potentielles. Les glissements rocheux d'Åknes, Tafjord et Rundefjellet en Norvège ont été surtout utilisés comme cas d'étude pour développer et tester les diverses approches. La surveillance d'instabilités de versant effectuée dans cette thèse de doctorat est essentiellement basée sur des MNT de haute résolution multi-temporels (ou séquentiels), en particulier des nuages de points séquentiels acquis par SLT. Les changements topographiques dus aux mouvements de versant peuvent être détectés et quantifiés sur l'ensemble d'un glissement, notamment par comparaisons des distances les plus courtes entre deux nuages de points. L'analyse détaillée des mouvements est basée sur la transformation affine entre la position initiale et finale d'un bloc et sa décomposition en mouvements translationnels et rotationnels. La surveillance par SLT a démontré son potentiel avec l'effondrement d'un pan de l'Eiger dans les Alpes suisses, mais aussi aux glissements rocheux d'Aknes et Nordnesfjellet en Norvège. Une des principales avancées à l'Eiger et à Aknes est la création de modèles d'instabilité cohérents en combinant la morphologie et l'agencement structural des sites avec les mesures de déplacements. Ces deux cas d'étude ont aussi démontré le fort contrôle des structures existantes dans le massif rocheux sur les directions de glissement. Le SLT a également été utilisé pour surveiller des glissements dans des terrains meubles comme dans les argiles sensibles au Québec (Canada), sur les berges de la rivière Sorge en Suisse et dans le chenal à laves torrentielles de l'Illgraben.Cette thèse de doctorat souligne le vaste champ d'applications des MNT de haute résolution et particulièrement du SLT dans la détection, l'analyse et la surveillance des mouvements de versant. Des études futures devraient explorer plus en profondeur les différentes techniques et approches développées, les améliorer et mieux les intégrer dans des pratiques actuelles d'analyse de danger et surtout dans la modélisation de stabilité des versants.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Experimental analysis and computer prediction of CTF/NFI transcription factor DNA binding sites.

Accurate prediction of transcription factor binding sites is needed to unravel the function and regulation of genes discovered in genome sequencing projects. To evaluate current computer prediction tools, we have begun a systematic study of the sequence-specific DNA-binding of a transcription factor belonging to the CTF/NFI family. Using a systematic collection of rationally designed oligonucleotides combined with an in vitro DNA binding assay, we found that the sequence specificity of this protein cannot be represented by a simple consensus sequence or weight matrix. For instance, CTF/NFI uses a flexible DNA binding mode that allows for variations of the binding site length. From the experimental data, we derived a novel prediction method using a generalised profile as a binding site predictor. Experimental evaluation of the generalised profile indicated that it accurately predicts the binding affinity of the transcription factor to natural or synthetic DNA sequences. Furthermore, the in vitro measured binding affinities of a subset of oligonucleotides were found to correlate with their transcriptional activities in transfected cells. The combined computational-experimental approach exemplified in this work thus resulted in an accurate prediction method for CTF/NFI binding sites potentially functioning as regulatory regions in vivo.

Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

Magnetoelectricity in the ferrimagnetic Cu2OSeO3: symmetry analysis and Raman scattering study

sublattices ferrimagnet Cu2OSeO3 with a cubic symmetry and a linear magnetoelectric effect. There is no spectroscopic evidence for structural lattice distortions below T-C=60 K, which are expected due to magnetoelectric coupling. Using symmetry arguments we explain this observation by considering a special type of ferrimagnetic ground state which does not generate a spontaneous electric polarization. Interestingly, Raman scattering shows a strong increase of electric polarization of media through a dynamic magnetoelectric effect as a remarkable enhancement of the scattering intensity below T-C. New lines of purely magnetic origin have been detected in the magnetically ordered state. A part of them are attributed as scattering on exchange magnons. Using this observation and further symmetry considerations we argue for strong Dzyaloshinskii-Moriya interaction existing in the Cu2OSeO3. (c) 2010 American Institute of Physics. [doi:10.1063/1.3455808]

Structural Analysis and Overlay Design of Pavements Using the Road Rater, MLR-84-3, 1986

In recent years the Iowa DOT has shifted emphasis from the construction of new roads to the maintenance and preservation of existing highways. A need has developed for analyzing pavements structurally to select the correct rehabilitation strategy and to properly design a pavement overlay if necessary. This need has been fulfilled by Road Rater testing which has been used successfully on all types of pavements to evaluate pavement and subgrade conditions and to design asphaltic concrete overlays. The Iowa Road Rater Design Method has been simplified so that it may be easily understood and used by the widely diverse groups of individuals which may be involved in pavement restoration and management. Road Rater analysis techniques have worked well to date and have been verified by pavement coring, soils sampling and testing, and pavement removal by block sampling. Void detection testing has also been performed experimentally in Iowa, and results indicate that the Road Rater can be used to locate pavement voids and that Road Rater analysis techniques are reasonably accurate. The success of Road Rater research and development has made deflection test data one of the most important pavement management inputs.