An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing

Satellite remote sensing of ocean colour is the only method currently available for synoptically measuring wide-area properties of ocean ecosystems, such as phytoplankton chlorophyll biomass. Recently, a variety of bio-optical and ecological methods have been established that use satellite data to identify and differentiate between either phytoplankton functional types (PFTs) or phytoplankton size classes (PSCs). In this study, several of these techniques were evaluated against in situ observations to determine their ability to detect dominant phytoplankton size classes (micro-, nano- and picoplankton). The techniques are applied to a 10-year ocean-colour data series from the SeaWiFS satellite sensor and compared with in situ data (6504 samples) from a variety of locations in the global ocean. Results show that spectral-response, ecological and abundance-based approaches can all perform with similar accuracy. Detection of microplankton and picoplankton were generally better than detection of nanoplankton. Abundance-based approaches were shown to provide better spatial retrieval of PSCs. Individual model performance varied according to PSC, input satellite data sources and in situ validation data types. Uncertainty in the comparison procedure and data sources was considered. Improved availability of in situ observations would aid ongoing research in this field. (C) 2010 Elsevier B.V. All rights reserved.

Application of different instrumental techniques to the selective evaluation of bio-sensible compounds in foodstuff of animal and vegetable origin

This PhD thesis describes the application of some instrumental analytical techniques suitable to the study of fundamental food products for the human diet, such as: extra virgin olive oil and dairy products. These products, widely spread in the market and with high nutritional values, are increasingly recognized healthy properties although their lipid fraction might contain some unfavorable components to the human health. The research activity has been structured in the following investigations: “Comparison of different techniques for trans fatty acids analysis” “Fatty acids analysis of outcrop milk cream samples, with particular emphasis on the content of Conjugated Linoleic Acid (CLA) and trans Fatty Acids (TFA), by using 100m high-polarity capillary column” “Evaluation of the oxidited fatty acids (OFA) content during the Parmigiano-Reggiano cheese seasoning” “Direct analysis of 4-desmethyl sterols and two dihydroxy triterpenes in saponified vegetal oils (olive oil and others) using liquid chromatography-mass spectrometry” “Quantitation of long chain poly-unsatured fatty acids (LC-PUFA) in base infant formulas by Gas Chromatography, and evaluation of the blending phases accuracy during their preparation” “Fatty acids composition of Parmigiano Reggiano cheese samples, with emphasis on trans isomers (TFA)”

Second law analysis and simulation techniques for the energy optimization of buildings

The research activity described in this thesis is focused mainly on the study of finite-element techniques applied to thermo-fluid dynamic problems of plant components and on the study of dynamic simulation techniques applied to integrated building design in order to enhance the energy performance of the building. The first part of this doctorate thesis is a broad dissertation on second law analysis of thermodynamic processes with the purpose of including the issue of the energy efficiency of buildings within a wider cultural context which is usually not considered by professionals in the energy sector. In particular, the first chapter includes, a rigorous scheme for the deduction of the expressions for molar exergy and molar flow exergy of pure chemical fuels. The study shows that molar exergy and molar flow exergy coincide when the temperature and pressure of the fuel are equal to those of the environment in which the combustion reaction takes place. A simple method to determine the Gibbs free energy for non-standard values of the temperature and pressure of the environment is then clarified. For hydrogen, carbon dioxide, and several hydrocarbons, the dependence of the molar exergy on the temperature and relative humidity of the environment is reported, together with an evaluation of molar exergy and molar flow exergy when the temperature and pressure of the fuel are different from those of the environment. As an application of second law analysis, a comparison of the thermodynamic efficiency of a condensing boiler and of a heat pump is also reported. The second chapter presents a study of borehole heat exchangers, that is, a polyethylene piping network buried in the soil which allows a ground-coupled heat pump to exchange heat with the ground. After a brief overview of low-enthalpy geothermal plants, an apparatus designed and assembled by the author to carry out thermal response tests is presented. Data obtained by means of in situ thermal response tests are reported and evaluated by means of a finite-element simulation method, implemented through the software package COMSOL Multyphysics. The simulation method allows the determination of the precise value of the effective thermal properties of the ground and of the grout, which are essential for the design of borehole heat exchangers. In addition to the study of a single plant component, namely the borehole heat exchanger, in the third chapter is presented a thorough process for the plant design of a zero carbon building complex. The plant is composed of: 1) a ground-coupled heat pump system for space heating and cooling, with electricity supplied by photovoltaic solar collectors; 2) air dehumidifiers; 3) thermal solar collectors to match 70% of domestic hot water energy use, and a wood pellet boiler for the remaining domestic hot water energy use and for exceptional winter peaks. This chapter includes the design methodology adopted: 1) dynamic simulation of the building complex with the software package TRNSYS for evaluating the energy requirements of the building complex; 2) ground-coupled heat pumps modelled by means of TRNSYS; and 3) evaluation of the total length of the borehole heat exchanger by an iterative method developed by the author. An economic feasibility and an exergy analysis of the proposed plant, compared with two other plants, are reported. The exergy analysis was performed by considering the embodied energy of the components of each plant and the exergy loss during the functioning of the plants.

Advanced CMOS Interfaces for Bio-Nanosensors

The improvement of devices provided by Nanotechnology has put forward new classes of sensors, called bio-nanosensors, which are very promising for the detection of biochemical molecules in a large variety of applications. Their use in lab-on-a-chip could gives rise to new opportunities in many fields, from health-care and bio-warfare to environmental and high-throughput screening for pharmaceutical industry. Bio-nanosensors have great advantages in terms of cost, performance, and parallelization. Indeed, they require very low quantities of reagents and improve the overall signal-to-noise-ratio due to increase of binding signal variations vs. area and reduction of stray capacitances. Additionally, they give rise to new challenges, such as the need to design high-performance low-noise integrated electronic interfaces. This thesis is related to the design of high-performance advanced CMOS interfaces for electrochemical bio-nanosensors. The main focus of the thesis is: 1) critical analysis of noise in sensing interfaces, 2) devising new techniques for noise reduction in discrete-time approaches, 3) developing new architectures for low-noise, low-power sensing interfaces. The manuscript reports a multi-project activity focusing on low-noise design and presents two developed integrated circuits (ICs) as examples of advanced CMOS interfaces for bio-nanosensors. The first project concerns low-noise current-sensing interface for DC and transient measurements of electrophysiological signals. The focus of this research activity is on the noise optimization of the electronic interface. A new noise reduction technique has been developed so as to realize an integrated CMOS interfaces with performance comparable with state-of-the-art instrumentations. The second project intends to realize a stand-alone, high-accuracy electrochemical impedance spectroscopy interface. The system is tailored for conductivity-temperature-depth sensors in environmental applications, as well as for bio-nanosensors. It is based on a band-pass delta-sigma technique and combines low-noise performance with low-power requirements.

Many-Core Architectures: Hardware-Software Optimization and Modeling Techniques

During the last few decades an unprecedented technological growth has been at the center of the embedded systems design paramount, with Moore’s Law being the leading factor of this trend. Today in fact an ever increasing number of cores can be integrated on the same die, marking the transition from state-of-the-art multi-core chips to the new many-core design paradigm. Despite the extraordinarily high computing power, the complexity of many-core chips opens the door to several challenges. As a result of the increased silicon density of modern Systems-on-a-Chip (SoC), the design space exploration needed to find the best design has exploded and hardware designers are in fact facing the problem of a huge design space. Virtual Platforms have always been used to enable hardware-software co-design, but today they are facing with the huge complexity of both hardware and software systems. In this thesis two different research works on Virtual Platforms are presented: the first one is intended for the hardware developer, to easily allow complex cycle accurate simulations of many-core SoCs. The second work exploits the parallel computing power of off-the-shelf General Purpose Graphics Processing Units (GPGPUs), with the goal of an increased simulation speed. The term Virtualization can be used in the context of many-core systems not only to refer to the aforementioned hardware emulation tools (Virtual Platforms), but also for two other main purposes: 1) to help the programmer to achieve the maximum possible performance of an application, by hiding the complexity of the underlying hardware. 2) to efficiently exploit the high parallel hardware of many-core chips in environments with multiple active Virtual Machines. This thesis is focused on virtualization techniques with the goal to mitigate, and overtake when possible, some of the challenges introduced by the many-core design paradigm.

NON-CHROMATOGRAPHIC PURIFICATION OF SYNTHETIC BIO-OLIGOMERS

Synthetic oligonucleotides and peptides have found wide applications in industry and academic research labs. There are ~60 peptide drugs on the market and over 500 under development. The global annual sale of peptide drugs in 2010 was estimated to be $13 billion. There are three oligonucleotide-based drugs on market; among them, the FDA newly approved Kynamro was predicted to have a $100 million annual sale. The annual sale of oligonucleotides to academic labs was estimated to be $700 million. Both bio-oligomers are mostly synthesized on automated synthesizers using solid phase synthesis technology, in which nucleoside or amino acid monomers are added sequentially until the desired full-length sequence is reached. The additions cannot be complete, which generates truncated undesired failure sequences. For almost all applications, these impurities must be removed. The most widely used method is HPLC. However, the method is slow, expensive, labor-intensive, not amendable for automation, difficult to scale up, and unsuitable for high throughput purification. It needs large capital investment, and consumes large volumes of harmful solvents. The purification costs are estimated to be more than 50% of total production costs. Other methods for bio-oligomer purification also have drawbacks, and are less favored than HPLC for most applications. To overcome the problems of known biopolymer purification technologies, we have developed two non-chromatographic purification methods. They are (1) catching failure sequences by polymerization, and (2) catching full-length sequences by polymerization. In the first method, a polymerizable group is attached to the failure sequences of the bio-oligomers during automated synthesis; purification is achieved by simply polymerizing the failure sequences into an insoluble gel and extracting full-length sequences. In the second method, a polymerizable group is attached to the full-length sequences, which are then incorporated into a polymer; impurities are removed by washing, and pure product is cleaved from polymer. These methods do not need chromatography, and all drawbacks of HPLC no longer exist. Using them, purification is achieved by simple manipulations such as shaking and extraction. Therefore, they are suitable for large scale purification of oligonucleotide and peptide drugs, and also ideal for high throughput purification, which currently has a high demand for research projects involving total gene synthesis. The dissertation will present the details about the development of the techniques. Chapter 1 will make an introduction to oligodeoxynucleotides (ODNs), their synthesis and purification. Chapter 2 will describe the detailed studies of using the catching failure sequences by polymerization method to purify ODNs. Chapter 3 will describe the further optimization of the catching failure sequences by polymerization ODN purification technology to the level of practical use. Chapter 4 will present using the catching full-length sequence by polymerization method for ODN purification using acid-cleavable linker. Chapter 5 will make an introduction to peptides, their synthesis and purification. Chapter 6 will describe the studies using the catching full-length sequence by polymerization method for peptide purification.

Design and Optimization of Power Delivery and Distribution Systems Using Evolutionary Computation Techniques

Nowadays computing platforms consist of a very large number of components that require to be supplied with diferent voltage levels and power requirements. Even a very small platform, like a handheld computer, may contain more than twenty diferent loads and voltage regulators. The power delivery designers of these systems are required to provide, in a very short time, the right power architecture that optimizes the performance, meets electrical specifications plus cost and size targets. The appropriate selection of the architecture and converters directly defines the performance of a given solution. Therefore, the designer needs to be able to evaluate a significant number of options in order to know with good certainty whether the selected solutions meet the size, energy eficiency and cost targets. The design dificulties of selecting the right solution arise due to the wide range of power conversion products provided by diferent manufacturers. These products range from discrete components (to build converters) to complete power conversion modules that employ diferent manufacturing technologies. Consequently, in most cases it is not possible to analyze all the alternatives (combinations of power architectures and converters) that can be built. The designer has to select a limited number of converters in order to simplify the analysis. In this thesis, in order to overcome the mentioned dificulties, a new design methodology for power supply systems is proposed. This methodology integrates evolutionary computation techniques in order to make possible analyzing a large number of possibilities. This exhaustive analysis helps the designer to quickly define a set of feasible solutions and select the best trade-off in performance according to each application. The proposed approach consists of two key steps, one for the automatic generation of architectures and other for the optimized selection of components. In this thesis are detailed the implementation of these two steps. The usefulness of the methodology is corroborated by contrasting the results using real problems and experiments designed to test the limits of the algorithms.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Efficient design and optimization of bio-photonic sensing cells (BICELLs) for label free biosensing

In previous works we demonstrated the benefits of using micro–nano patterning materials to be used as bio-photonic sensing cells (BICELLs), referred as micro–nano photonic structures having immobilized bioreceptors on its surface with the capability of recognizing the molecular binding by optical transduction. Gestrinone/anti-gestrinone and BSA/anti-BSA pairs were proven under different optical configurations to experimentally validate the biosensing capability of these bio-sensitive photonic architectures. Moreover, Three-Dimensional Finite Difference Time Domain (FDTD) models were employed for simulating the optical response of these structures. For this article, we have developed an effective analytical simulation methodology capable of simulating complex biophotonic sensing architectures. This simulation method has been tested and compared with previous experimental results and FDTD models. Moreover, this effective simulation methodology can be used for efficiently design and optimize any structure as BICELL. In particular for this article, six different BICELL's types have been optimized. To carry out this optimization we have considered three figures of merit: optical sensitivity, Q-factor and signal amplitude. The final objective of this paper is not only validating a suitable and efficient optical simulation methodology but also demonstrating the capability of this method for analyzing the performance of a given number of BICELLs for label-free biosensing.

Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. It has evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Optimization of the bridged-T network using computer techniques,

Issued also as thesis (M.S.) University of Illinois.

The construction and optimization of a surface plasmon resonance sensor for the studies of bio-molecular interactions

Bio-molecular interactions exist ubiquitously in all biological systems. This dissertation project was to construct a powerful surface plasmon resonance (SPR) sensor. The SPR system is used to study bio-molecular interactions in real time and without labeling. Surface plasmon is the oscillation of free electrons in metals coupled with surface electromagnetic waves. These surface electromagnetic waves provide a sensitive probe to study bio-molecular interactions on metal surfaces. This project resulted in the successful construction and optimization of a homemade SPR sensor and the development of several new powerful protocols to study bio-molecular interactions. It was discovered through this project that the limitations of earlier SPR sensors are related not only to the instrumentation design and operating procedures, but also to the complex behaviors of bio-molecules on sensor surfaces that were very different from that in solution. Based on these discoveries the instrumentation design and operating procedures were fully optimized. A set of existing sensor surface treatment protocols were tested and evaluated and new protocols were developed in this project. The new protocols have demonstrated excellent performance to study biomolecular interactions. The optimized home-made SPR sensor was used to study protein-surface interactions. These protein-surface interactions are responsible for many complex organic cell activities. The co-existence of different driving forces and their correlation with the structure of the protein and the surface make the understanding of the fundamental mechanism of protein-surface interactions a very challenging task. Using the improved SPR sensor, the electrostatic interaction and hydrophobic interaction were studied separately. The results of this project directly confirmed the theoretical predictions for electrostatic force between the protein and surface. In addition, this project demonstrated that the strength of the protein-surface hydrophobic interaction does not solely depend on the hydrophobicity as reported earlier. Surface structure also plays a significant role.

From theory-inspired to theory-based interventions : A protocol for developing and testing a methodology for linking behaviour change techniques to theoretical mechanisms of action

This research is funded by UK Medical Research Council grant number MR/L011115/1

From Theory-Inspired to Theory-Based Interventions : A Protocol for Developing and Testing a Methodology for Linking Behaviour Change Techniques to Theoretical Mechanisms of Action

This research is funded by UK Medical Research Council grant number MR/L011115/1. We would like to thank the 105 experts in behaviour change who have committed their time and offered their expertise for study 2 of this research. We are also very grateful to all those who sent us peer-reviewed behaviour change intervention descriptions for study 1. Finally, we would like thank Dr. Emma Beard and Dr. Dan Dediu for their statistical input and to all the researchers, particularly Holly Walton, who have assisted in the coding of papers for study 1.