Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.

Phase mask selection in wavefront coding systems: an adaptive approach

A method for optimizing the strength of a parametric phase mask for a wavefront coding imaging system is presented. The method is based on an optimization process that minimizes a proposed merit function. The goal is to achieve modulation transfer function invariance while quantitatively maintaining nal image delity. A parametric lter that copes with the noise present in the captured images is used to obtain the nal images, and this lter is optimized. The whole process results in optimum phase mask strength and optimal parameters for the restoration lter. The results for a particular optical system are presented and tested experimentally in the labo- ratory. The experimental results show good agreement with the simulations, indicating that the procedure is useful.

Multifactor Performance Study on Complex Systems for the Benefit of Management

The primary objective is to identify the critical factors that have a natural impact on the performance measurement system. It is important to make correct decisions related to measurement systems, which are based on the complex business environment. The performance measurement system is combined with a very complex non-linear factor. The Six Sigma methodology is seen as one potential approach at every organisational level. It will be linked to the performance and financial measurement as well as to the analytical thinking on which the viewpoint of management depends. The complex systems are connected to the customer relationship study. As the primary throughput can be seen in a new well-defined performance measurement structure that will also be facilitated as will an analytical multifactor system. These critical factors should also be seen as a business innovation opportunity at the same time. This master's thesis has been divided into two different theoretical parts. The empirical part consists of both action-oriented and constructive research approaches with an empirical case study. The secondary objective is to seek a competitive advantage factor with a new analytical tool and the Six Sigma thinking. Process and product capabilities will be linked to the contribution of complex system. These critical barriers will be identified by the performance measuring system. The secondary throughput can be recognised as the product and the process cost efficiencies which throughputs are achieved with an advantage of management. The performance measurement potential is related to the different productivity analysis. Productivity can be seen as one essential part of the competitive advantage factor.

Mediterranean-climate streams and rivers: geographically separated but ecologically comparable freshwater systems

Streams and rivers in mediterranean-climate regions (med-rivers in med-regions) are ecologically unique, with flow regimes reflecting precipitation patterns. Although timing of drying and flooding is predictable, seasonal and annual intensity of these events is not. Sequential flooding and drying, coupled with anthropogenic influences make these med-rivers among the most stressed riverine habitat worldwide. Med-rivers are hotspots for biodiversity in all med-regions. Species in med-rivers require different, often opposing adaptive mechanisms to survive drought and flood conditions or recover from them. Thus, metacommunities undergo seasonal differences, reflecting cycles of river fragmentation and connectivity, which also affect ecosystem functioning. River conservation and management is challenging, and trade-offs between environmental and human uses are complex, especially under future climate change scenarios. This overview of a Special Issue on med-rivers synthesizes information presented in 21 articles covering the five med-regions worldwide: Mediterranean Basin, coastal California, central Chile, Cape region of South Africa, and southwest and southern Australia. Research programs to increase basic knowledge in less-developed med-regions should be prioritized to achieve increased abilities to better manage med-rivers.

Combining Generic Programming and Service-Oriented Architectures for the Effective and Timely Development of Complex e-Learning Systems

Peer-reviewed

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Polysaccharide-based Polyion Complex Micelles as New Delivery Systems for Hydrophilic Cationic Drugs

Les micelles polyioniques ont émergé comme des systèmes prometteurs de relargage de médicaments hydrophiles ioniques. Le but de cette étude était le développement des micelles polyioniques à base de dextrane pour la relargage de médicaments hydrophiles cationiques utilisant une nouvelle famille de copolymères bloc carboxymethyldextran-poly(éthylène glycol) (CMD-PEG). Quatre copolymères CMD-PEG ont été préparés dont deux copolymères identiques en termes de longueurs des blocs de CMD et de PEG mais différent en termes de densité de charges du bloc CMD; et deux autres copolymères dans lesquels les blocs chargés sont les mêmes mais dont les blocs de PEG sont différents. Les propriétés d’encapsulation des micelles CMD-PEG ont été évaluées avec différentes molécules cationiques: le diminazène (DIM), un médicament cationique modèle, le chlorhydrate de minocycline (MH), un analogue semi-synthétique de la tétracycline avec des propriétés neuro-protectives prometteuses et différents antibiotiques aminoglycosidiques. La cytotoxicité des copolymères CMD-PEG a été évaluée sur différentes lignées cellulaires en utilisant le test MTT et le test du Bleu Alamar. La formation de micelles des copolymères de CMD-PEG a été caractérisée par différentes techniques telles que la spectroscopie RMN 1H, la diffusion de la lumière dynamique (DLS) et la titration calorimétrique isotherme (ITC). Le taux de relargage des médicaments et l’activité pharmacologique des micelles contenant des médicaments ont aussi été évalués. Les copolymères CMD-PEG n'ont induit aucune cytotoxicité dans les hépatocytes humains et dans les cellules microgliales murines (N9) après 24 h incubation pour des concentrations allant jusqu’à 15 mg/mL. Les interactions électrostatiques entre les copolymères de CMD-PEG et les différentes drogues cationiques ont amorcé la formation de micelles polyioniques avec un coeur composé du complexe CMD-médicaments cationiques et une couronne composée de PEG. Les propriétés des micelles DIM/CMDPEG ont été fortement dépendantes du degré de carboxyméthylation du bloc CMD. Les micelles de CMD-PEG de degré de carboxyméthylation du bloc CMD ≥ 60 %, ont incorporé jusqu'à 64 % en poids de DIM et ont résisté à la désintégration induite par les sels et ceci jusqu'à 400 mM NaCl. Par contre, les micelles de CMD-PEG de degré de carboxyméthylation ~ 30% avaient une plus faible teneur en médicament (~ 40 % en poids de DIM) et se désagrégeaient à des concentrations en sel inférieures (∼ 100 mM NaCl). Le copolymère de CMD-PEG qui a montré les propriétés micellaires les plus satisfaisantes a été sélectionné comme système de livraison potentiel de chlorhydrate de minocycline (MH) et d’antibiotiques aminoglycosidiques. Les micelles CMD-PEG encapsulantes de MH ou d’aminoglycosides ont une petite taille (< 200 nm de diamètre), une forte capacité de chargement (≥ 50% en poids de médicaments) et une plus longue période de relargage de médicament. Ces micelles furent stables en solution aqueuse pendant un mois; après lyophilisation et en présence d'albumine sérique bovine. De plus, les micelles ont protégé MH contre sa dégradation en solutions aqueuses. Les micelles encapsulant les drogues ont maintenu les activités pharmacologiques de ces dernières. En outre, les micelles MH réduisent l’inflammation induite par les lipopolysaccharides dans les cellules microgliales murines (N9). Les micelles aminoglycosides ont été quant à elles capable de tuer une culture bactérienne test. Toutefois les micelles aminoglycosides/CMDPEG furent instables dans les conditions physiologiques. Les propriétés des micelles ont été considérablement améliorées par des modifications hydrophobiques de CMD-PEG. Ainsi, les micelles aminoglycosides/dodecyl-CMD-PEG ont montré une taille plus petite et une meilleure stabilité aux conditions physiologiques. Les résultats obtenus dans le cadre de cette étude montrent que CMD-PEG copolymères sont des systèmes prometteurs de relargage de médicaments cationiques.

Studies on function projective synchronization in chaotic and hyperchaotic systems

FPS is a more general form of synchronization. Hyperchaotic systems possessing more than one positive Lypaunov exponent exhibit highly complex behaviour and are more suitable for some applications like secure communications. In this thesis we report studies of FPS and MFPS of a few chaotic and hyperchaotic systems. When all the parameters of the system are known we show that active nonlinear control method can be efectively used to obtain FPS. Adaptive nonlinear control and OPCL control method are employed for obtaining FPS and MFPS when some or all parameters of the system are uncertain. A secure communication scheme based on MFPS is also proposed in theory. All our theoretical calculations are verified by numerical simulations.

Mapping fibre orientation in complex-shaped biological systems with micrometre resolution by scanning X-ray microdiffraction

A fully automated procedure to extract and to image local fibre orientation in biological tissues from scanning X-ray diffraction is presented. The preferred chitin fibre orientation in the flow sensing system of crickets is determined with high spatial resolution by applying synchrotron radiation based X-ray microbeam diffraction in conjunction with advanced sample sectioning using a UV micro-laser. The data analysis is based on an automated detection of azimuthal diffraction maxima after 2D convolution filtering (smoothing) of the 2D diffraction patterns. Under the assumption of crystallographic fibre symmetry around the morphological fibre axis, the evaluation method allows mapping the three-dimensional orientation of the fibre axes in space. The resulting two-dimensional maps of the local fibre orientations - together with the complex shape of the flow sensing system - may be useful for a better understanding of the mechanical optimization of such tissues.

Articulation of stakeholders requirements for complex e-government systems development

Requirements analysis focuses on stakeholders concerns and their influence towards e-government systems. Some characteristics of stakeholders concerns clearly show the complexity and conflicts. This imposes a number of questions in the requirements analysis, such as how are they relevant to stakeholders? What are their needs? How conflicts among the different stakeholders can be resolved? And what coherent requirements can be methodologically produced? This paper describes the problem articulation method in organizational semiotics which can be used to conduct such complex requirements analysis. The outcomes of the analysis enable e-government systems development and management to meet userspsila needs. A case study of Yantai Citizen Card is chosen to illustrate a process of analysing stakeholders in the lifecycle of requirements analysis.

Modelling complex systems for project planning - A semiotics motivated method

A large and complex IT project may involve multiple organizations and be constrained within a temporal period. An organization is a system comprising of people, activities, processes, information, resources and goals. Understanding and modelling such a project and its interrelationship with relevant organizations are essential for organizational project planning. This paper introduces the problem articulation method (PAM) as a semiotic method for organizational infrastructure modelling. PAM offers a suite of techniques, which enables the articulation of the business, technical and organizational requirements, delivering an infrastructural framework to support the organization. It works by eliciting and formalizing (e. g. processes, activities, relationships, responsibilities, communications, resources, agents, dependencies and constraints) and mapping these abstractions to represent the manifestation of the "actual" organization. Many analysts forgo organizational modelling methods and use localized ad hoc and point solutions, but this is not amenable for organizational infrastructures modelling. A case study of the infrared atmospheric sounding interferometer (IASI) will be used to demonstrate the applicability of PAM, and to examine its relevancy and significance in dealing with the innovation and changes in the organizations.

Adaptive deadbeat control of stochastic systems

This paper considers the use of a discrete-time deadbeat control action on systems affected by noise. Variations on the standard controller form are discussed and comparisons are made with controllers in which noise rejection is a higher priority objective. Both load and random disturbances are considered in the system description, although the aim of the deadbeat design remains as a tailoring of reference input variations. Finally, the use of such a deadbeat action within a self-tuning control framework is shown to satisfy, under certain conditions, the self-tuning property, generally though only when an extended form of least-squares estimation is incorporated.