Recent experiments suggest that the human genome (all of our DNA) is organised as a so-called fractal globule. The fractal globule is a knot--free dense polymer that easily folds and unfolds any genomic locus, for example a group of nearby genes. Proteins often need to locate specific target sites on the DNA, for instance to activate a gene. To understand how proteins move through the DNA polymer, we simulate diffusion of particles through a fractal globule. The fractal globule was generated on a cubic lattice as spheres connected by cylinders. With the structure in place, we simulate particle diffusion and measure how their mean squared displacement ($\langle R^2(t)\rangle$) grows as function of time $t$ for different particle radii. This quantity allows us to better understand how the three dimensional structure of DNA affects the protein's motion. From our simulations we found that $\langle R^2(t)/t\rangle$ is a decaying function when the particle is sufficiently large. This means that the particles diffuse slower than if they were free. Assuming that $\langle R^2(t) \rangle \propto t^\alpha$ for long times, we calculated the growth exponent $\alpha$ as a function of particle radius $r_p$. When $r_p$ is small compared to the average distance between two polymer segments $d$, we find that $\alpha \approx 1$. This means the polymer network does not affect the particle's motion. However, in the opposite limit $r_p\sim d$ we find that $\alpha<1$ which means that the polymer strongly slows down the particle's motion. This behaviour is indicative of sub-diffusive dynamics and has potentially far reaching consequences for target finding processes and biochemical reactions in the cell.

Une performance soutenable et importante est l'objectif principal du management de toute organisation. La viabilité d'une entreprise dépend de sa capacité à trouver un équilibre entre deux activités très différentes. D’une part elle doit exploiter les certitudes existantes pour garantir la réussite des opérations courantes et d’autre part explorer de nouvelles opportunités pour la mise en œuvre rapide des nouvelles idées qui garantissent l'avenir de l'organisation. Les organisations ambidextres ont une aptitude à poursuivre de manière simultanée ces activités et produisent des innovation radicales et incrémentielles. Notre recherche montre que pour être ambidextre seulement la séparation des activités n’est pas suffisante. Sans une intégration et une combinaison de ces structures et ces processus, l’unité d’exploration sera incapable d’exploiter ses résultats et a plus de chance disparaitre avec le temps comme la structure inefficace. Pour survivre sur le long terme, l’entreprise doit intégrer l’ambidexterité de manière fractale et dynamique. Ce nouveau concept propose une solution à la question de l’équilibre entre les activités et elle se définit comme la capacité organisationnelle à reproduire l’exploration et l’exploitation simultanément à différents niveaux organisationnels et être capable de changer leurs proportions quand cela est nécessaire / The main objective in management of any organization is a successful and sustainable performance. To survive over time, a company should combine two competing activities. On the one hand, it must exploit existing certainties to be effective in the short term, and on the other hand, being capable at the same time to explore new opportunities to be innovative in the future. Ambidextrous organizations have the ability to pursue these activities simultaneously and produce radical and incremental innovation. In our research, we show that to achieve ambidexterity separation of activities is important, but not sufficient. Without integration between business structures and processes, the exploratory activity of the innovation unit is more likely to shrink and disappear with time as unprofitable function. To survive and sustain in the long term, a company should adapt fractal and dynamic ambidexterity. This new concept provides us with a solution to the question of balance and determines the organizational ability to define and set the appropriate proportions of exploration and exploitation simultaneously at multiple organizational levels and re-configure them when it is necessary to meet the change.

The present study considers the dependence of mechanical properties in composite laminates on the fibre architecture. The objective is to characterise the mechanical properties of composite plates while varying the fibre distribution but keeping the constituent materials unchanged. Image analysis and fractal dimension have been used to quantify fibre distribution and resin-rich volumes (RRV) and to correlate these with the mechanical properties of the fibre-reinforced composites. The formation, shape and size of RRV in composites with different fabric architectures is discussed. The majority of studies in literatures show a negative effect of the RRV on the mechanical behaviour of composite materials. RRV arise primarily as a result of (a) the clustering of fibres as bundles in textiles, (b) the stacking sequence, and/ or stacking process, (c) the resin properties and flow characteristics, (d) the heating rate as this directly affects viscosity and (e) the consolidation pressure. Woven glass and carbon/epoxy fabric composites were manufactured either by the infusion or the resin transfer moulding (RTM) process. The fractal dimension (D) has been employed to explore the correlation between fabric architecture and mechanical properties (in glass or/ carbon fibre reinforced composites with different weave styles and fibre volume fraction). The fractal dimension was determined using optical microscopy images and ImageJ with FracLac software, and the D has been correlated with the flexural modulus, ultimate flexural strength (UFS), interlaminar shear strength (ILSS) and the fatigue properties of the woven carbon/epoxy fabric composites. The present study also considers the dependence of fatigue properties in composite laminates on static properties and fibre architecture. Four-point flexural fatigue test was conducted under load control, at sinusoidal frequency of 10 Hz with amplitude control. Using a stress ratio (R=σmin/σmax) of 0.1 for the tension side and 10 for the compression side, specimens were subjected to maximum fatigue stresses of 95% to 82.5% step 2.5% of the ultimate flexural strength (UFS). The fatigue data were correlated with the static properties and the fibre distribution, in order to obtain a useful general description of the laminate behaviour under flexural fatigue load. The analysis of variance (ANOVA) technique was applied to the results obtained to identify statistically the significance of the correlations. Composite strength and ILSS show a clear dependence on the fibre distribution quantified using D. For the carbon fabric architectures considered in this study, the fatigue properties of composite laminates have significant correlations with the fibre distribution and the static properties of the laminates. The loss of 5-6 % in the flexural modulus of composite laminates indicates an increasing risk of failure of the composite laminates under fatigue loads. The endurance limits, based on either the static properties or the fibre distribution, were inversely proportional to the strength for all laminates.

Fabrics are unique materials which consist of various properties affecting their performance and end-uses. A computerized fabric property evaluation and analysis method plays a crucial role not only in textile industry but also in scientific research. An accurate analysis and measurement of fabric property provides a powerful tool for gauging product quality, assuring regulatory compliance and assessing the performance of textile materials. This thesis investigated the solutions for applying computerized methods to evaluate and intelligently interpret the texture and material properties of fabric in an inexpensive and efficient way. Firstly, a method which allows automatic recognition of basic weave pattern and precisely measuring the yarn count is proposed. The yarn crossed-areas are segmented by a spatial domain integral projection approach. Combining fuzzy c-means (FCM) and principal component analysis (PCA) on grey level co-occurrence matrix (GLCM) feature vectors extracted from the segments enables to classify detected segments into two clusters. Based on the analysis on texture orientation features, the yarn crossed-area states are automatically determined. An autocorrelation method is used to find weave repeats and correct detection errors. The method was validated by using computer simulated woven samples and real woven fabric images. The test samples have various yarn counts, appearance, and weave types. All weave patterns of tested fabric samples are successfully recognized and computed yarn counts are consistent to the manual counts. Secondly, we present a methodology for using the high resolution 3D surface data of fabric samples to measure surface roughness in a nondestructive and accurate way. A parameter FDFFT, which is the fractal dimension estimation from 2DFFT of 3D surface scan, is proposed as the indicator of surface roughness. The robustness of FDFFT, which consists of the rotation-invariance and scale-invariance, is validated on a number of computer simulated fractal Brownian images. Secondly, in order to evaluate the usefulness of FDFFT, a novel method of calculating standard roughness parameters from 3D surface scan is introduced. According to the test results, FDFFT has been demonstrated as a fast and reliable parameter for measuring the fabric roughness from 3D surface data. We attempt a neural network model using back propagation algorithm and FDFFT for predicting the standard roughness parameters. The proposed neural network model shows good performance experimentally. Finally, an intelligent approach for the interpretation of fabric objective measurements is proposed using supported vector machine (SVM) techniques. The human expert assessments of fabric samples are used during the training phase in order to adjust the general system into an applicable model. Since the target output of the system is clear, the uncertainty which lies in current subjective fabric evaluation does not affect the performance of proposed model. The support vector machine is one of the best solutions for handling high dimensional data classification. The complexity problem of the fabric property has been optimally dealt with. The generalization ability shown in SVM allows the user to separately implement and design the components. Sufficient cross-validations are performed and demonstrate the performance test of the system.

As the failure of a bearing could cause cascading breakdowns of the mechanical system and then lead to costly repairs and production delays, bearing condition monitoring has received much attention for decades. One of the primary methods for this purpose is based on the analysis of vibration signal measured by accelerometers because such data are information-rich. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault-generated impulses, interferences from other machine components, and background noise, where fault-induced impulses are further modulated by various low frequency signal contents. The compounded effects of interferences, background noise and the combined modulation effects make it difficult to detect bearing faults. This is further complicated by the nonstationary nature of vibration signals due to speed variations in some cases, such as the bearings in a wind turbine. As such, the main challenges in the vibration-based bearing monitoring are how to address the modulation, noise, interference, and nonstationarity matters. Over the past few decades, considerable research activities have been carried out to deal with the first three issues. Recently, the nonstationarity matter has also attracted strong interests from both industry and academic community. Nevertheless, the existing techniques still have problems (deficiencies) as listed below: (1) The existing enveloping methods for bearing fault feature extraction are often adversely affected by multiple interferences. To eliminate the effect of interferences, the prefiltering is required, which is often parameter-dependent and knowledge-demanding. The selection of proper filter parameters is challenging and even more so in a time-varying environment. (2) Even though filters are properly designed, they are of little use in handling in-band noise and interferences which are also barriers for bearing fault detection, particularly for incipient bearing faults with weak signatures. (3) Conventional approaches for bearing fault detection under constant speed are no longer applicable to the variable speed case because such speed fluctuations may cause “smearing” of the discrete frequencies in the frequency representation. Most current methods for rotating machinery condition monitoring under time-varying speed require signal resampling based on the shaft rotating frequency. For the bearing case, the shaft rotating frequency is, however, often unavailable as it is coupled with the instantaneous fault characteristic frequency (IFCF) by a fault characteristic coefficient (FCC) which cannot be determined without knowing the fault type. Additionally, the effectiveness of resampling-based methods is largely dependent on the accuracy of resampling procedure which, even if reliable, can complicate the entire fault detection process substantially. (4) Time-frequency analysis (TFA) has proved to be a powerful tool in analyzing nonstationary signal and moreover does not require resampling for bearing fault identification. However, the diffusion of time-frequency representation (TFR) along time and frequency axes caused by lack of energy concentration would handicap the application of the TFA. In fact, the reported TFA applications in bearing fault diagnosis are still very limited. To address the first two aforementioned problems, i.e., (1) and (2), for constant speed cases, two morphology-based methods are proposed to extract bearing fault feature without prefiltering. Another two methods are developed to specifically handle the remaining problems for the bearing fault detection under time-varying speed conditions. These methods are itemized as follows: (1) An efficient enveloping method based on signal Fractal Dimension (FD) for bearing fault feature extraction without prefiltering, (2) A signal decomposition technique based on oscillatory behaviors for noise reduction and interferences removal (including in-band ones), (3) A prefiltering-free and resampling-free approach for bearing fault diagnosis under variable speed condition via the joint application of FD-based envelope demodulation and generalized demodulation (GD), and (4) A combined dual-demodulation transform (DDT) and synchrosqueezing approach for TFR energy concentration level enhancement and bearing fault identification. With respect to constant speed cases, the FD-based enveloping method, where a short time Fractal dimension (STFD) transform is proposed, can suppress interferences and highlight the fault-induced impulsive signature by transforming the vibration signal into a STFD representation. Its effectiveness, however, deteriorates with the increased complexity of the interference frequencies, particularly for multiple interferences with high frequencies. As such, the second method, which isolates fault-induced transients from interferences and noise via oscillatory behavior analysis, is then developed to complement the FD-based enveloping approach. Both methods are independent of frequency information and free from prefiltering, hence eliminating the tedious process for filter parameter specification. The in-band vibration interferences can also be suppressed mainly by the second approach. For the nonstationary cases, a prefiltering-free and resampling-free strategy is developed via the joint application of STFD and GD, from which a resampling-free order spectrum can be derived. This order spectrum can effectively reveal not only the existence of a fault but also its location. However, the success of this method relies largely on an effective enveloping technique. To address this matter and at the same time to exploit the advantages of TFA in nonstationary signal analysis, a TFA technique, involving dual demodulations and an iterative process, is developed and innovatively applied to bearing fault identification. The proposed methods have been validated using both simulation and experimental data collected in our lab. The test results have shown that the first two methods can effectively extract fault signatures, remove the interferences (including in-band ones) without prefiltering, and detect fault types from vibration signals for constant speed cases. The last two have shown to be effective in detecting faults and discern fault types from vibration data collected under variable speed conditions without resampling and prefiltering.

L’incorporation de silice, obtenue par un procédé de précipitation en milieu aqueux, à la bande de roulement des pneumatiques a permis d’en réduire significativement la résistance au roulement et par conséquent, l’impact environnemental. L’efficacité de la silice précipitée en tant que charge de renfort est liée à la présence d’agrégats nanométriques au sein de ce matériau et à son interaction avec l’élastomère du pneumatique. La maîtrise de la morphologie des agrégats est donc un levier pour le développement de silices plus performantes. Dans ce contexte, la présente étude porte sur le développement d’un modèle prédictif de la formation de l’agrégat de silice au cours de la précipitation. Un suivi par turbidimétrie en ligne et par DLS a permis d’illustrer l’influence critique des paramètres de synthèse sur la cinétique d’agrégation. Un modèle optique basé sur les propriétés diffusantes des objets fractals a été développé pour extraire des informations morphologiques sur l’agrégat au cours de sa construction à partir des spectres de turbidité expérimentaux. Les résultats semblent indiquer une densification de la structure au fur et à mesure que se déroule l’agrégation. Les analyses de porosimétrie azote et mercure menées sur les produits finaux, obtenus après séchage, ont quant à elles mis en évidence des différences texturales qui ont pu être mises en lien avec la cinétique d’agrégation. L’ensemble de ces informations a été repris dans un bilan de population permettant de traiter à la fois la croissance et l’agrégation des particules de silice ainsi que de simuler les propriétés optiques de la suspension. / Tires manufactured with precipitated silica instead of carbon black present a significantly lower rolling resistance and are therefore more environmentally friendly. Existence of nanometric aggregates inside the precipitated silica is responsible for its efficiency as a reinforcing filler. This level of structure deeply affects the quality of the interactions between silica and the rubber of the tire tread. Gaining control over the morphology of the aggregates could thus be a way to produce silica even more suited to this application.The aim of the present study is to develop a theoretical model able to predict the formation of silica aggregates during the precipitation process. Critical influence of the synthesis parameters on the aggregation kinetics were evidenced by DLS and online turbidimetry measurements. Morphological parameters of the expanding aggregates could be extracted from the experimental turbidity spectra thanks to a fractal scattering optical model. The observed trend suggested a densification of the aggregates over time. Nitrogen and mercury porosimetry analyses were carried out on the dried powders obtained at the end of the precipitation. Differences between the characterized samples could be related to the variations in their aggregation kinetics. Finally, a population balance model was developed. A specific feature of our model is that it is able to take into account both growth and aggregation of silica particles as well as to simulate their optical properties.

O volume de informação manipulada em sistemas apoiados por computador tem crescido tanto no número de objetos que compõem os conjuntos de dados quanto na quantidade e na complexidade dos atributos. Em conjuntos de dados do mundo real, a uniformidade na distribuição de valores e a independência entre atributos são propriedades bastante incomuns. De fato, dados reais são em geral caracterizados pela ampla presença de correlações entre seus atributos. Além disso, num mesmo conjunto podem existir correlações de naturezas diversas, como correlações lineares, não-lineares e não-polinomiais. Todo esse cenário pode degradar a performance dos algoritmos que manipulam e, principalmente, dos que realizam análises dos dados. Além da grande quantidade de objetos a serem tratados e do número elevado de atributos, as correlações nem sempre são conhecidas, o que pode comprometer a eficácia de tais algoritmos. Nesse contexto, as técnicas de redução de dimensionalidade permitem diminuir o número de atributos de um conjunto de dados, minimizando assim os problemas decorrentes da alta dimensionalidade. Algumas delas são baseadas na análise de correlações e, com o objetivo de reduzir a perda de informação relevante causada pela remoção de atributos, procuram eliminar apenas aqueles que sejam correlacionados aos restantes. No entanto, essas técnicas geralmente analisam como cada atributo está correlacionado a todos os demais, tratando o conjunto de atributos como um todo e usando ferramentas de análise estatística. Esta tese propõe uma abordagem diferente, baseada na Teoria dos Fractais, para detectar a existência de correlações e identificar subconjuntos de atributos correlacionados. Para cada correlação encontrada é possível ainda identificar quais são os atributos que melhor a descrevem. Conseqüentemente, um subconjunto de atributos relevantes para representar as características fundamentais dos dados é determinado, não apenas com base em correlações globais entre todos os atributos, mas também levando em consideração especificidades de correlações que envolvem subconjuntos reduzidos. A técnica apresentada é uma ferramenta a ser utilizada em etapas de pré-processamento de atividades de descoberta de conhecimento, principalmente em operações de seleção de atributos para redução de dimensionalidade. A proposta para a identificação de correlações e os conceitos que a fundamentam são validados por meio de estudos experimentais usando tanto dados sintéticos quanto reais. Finalmente, os conceitos básicos da Teoria dos Fractais são aplicados na análise de comportamento de data streams, também constituindo uma contribuição relevante desta tese de doutorado. / The volume of information processed by computer-based systems has grown not only in the amount of data but also in number and complexity of attributes. In real world datasets, uniform value distribution and independence between attributes are rather uncommon properties. In fact, real data is usually characterized by vast existence of correlated attributes. Moreover, a dataset can present different types of correlations, such as linear, non-linear and non-polynomial. This entire scenario may degrade performance of data management and, particularly, data analysis algorithms, as they need to deal with large amount of data and high number of attributes. Furthermore, correlations are usually unknown, which may jeopardize the efficacy of these algorithms. In this context, dimensionality reduction techniques can reduce the number of attributes in datasets, thus minimizing the problems caused by high dimensionality. Some of these techniques are based on correlation analysis and try to eliminate only attributes that are correlated to those remaining, aiming at diminishing the loss of relevant information imposed by attribute removal. However, techniques proposed so far usually analyze how each attribute is correlated to all the others, considering the attribute set as a whole and applying statistical analysis tools. This thesis presents a different approach, based on the Theory of Fractals, to detect the existence of correlations and to identify subsets of correlated attributes. In addition, the proposed technique makes it possible to identify which attributes can better describe each correlation. Consequently, a subset of attributes relevant to represent the fundamental characteristics of the dataset is determined, not only based on global correlations but also considering particularities of correlations concerning smaller attribute subsets. The proposed technique works as a tool to be used in preprocessing steps of knowledge discovery activities, mainly in feature selection operations for dimensionality reduction. The technique of correlation detection and its main concepts are validated through experimental studies with synthetic and real data. Finally, as an additional relevant contribution of this thesis, the basic concepts of the Theory of Fractals are also applied to analyze data streams behavior.

Practical combustion devices such as gas turbines and diesel engines operate at high pressures to increase their efficiency. Pressure significantly increases the overall soot yield. Morphology of these ultra-fine particles determines their airborne lifetime and their interaction with the human respiratory system. Therefore, investigating soot morphology at high pressure is of practical relevance. In this work, a novel experimental setup has been designed and built to study the soot morphology at elevated pressures. The experimental setup consists of a pressure vessel, which can provide optical access from 10° to 165° for multi-angle light scattering, and a counterflow burner which produces laminar flames at elevated pressures. In the first part of the study, N2-diluted ethylene/air and ethane air counterflow flames are stabilized from 2 to 5 atm. Two-angle light scattering and extinction technique have been used to study the effects of pressure on soot parameters. Path averaged soot volume fraction is found to be very sensitive to pressure and increased significantly from 2 to 5 atm. Primary particle size and aggregate size also increased with pressure. Multi-angle light scattering is also performed and flames are investigated from 3 to 5 atm. Scattering to absorption ratio is calculated from multi-angle light scattering and extinction data. Scattering to absorption ratio increased with pressure whereas the number of primary particles in an aggregate decreased with increasing pressure. In the next part of the study, Thermophoretic Sampling of soot is performed, in counterflow flames from 3 to 10 atm, followed by transmission electron microscopy. Mean primary particle size increased with pressure and these trends are consistent withour light scattering measurements. Fractal properties of soot aggregates are found to be insensitive to pressure. 2D diffused light line of sight attenuation (LOSA) and Laser Induced Incandescence (LII) are used to measure local soot volume fraction from 2 to 10 atm. Local soot volume fraction increased with pressure and soot concentration profiles showed good agreements when measured by both techniques. Experimental data obtained in this work is very helpful for the modelers for validating their codes and predicting the soot formation in pressurized flames.

京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第22534号 / 人博第937号 / 新制||人||223(附属図書館) / 2019||人博||937(吉田南総合図書館) / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 角 大輝, 教授 上木 直昌, 准教授 木坂 正史 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

Graphene electronics represent a developing field where many material properties and devices characteristics are still unknown. Researching several possible fabrication processes creates a fabrication process using resources found at Cal Poly a local industry sponsor. The project attempts to produce a graphene network in the shape of a fractal Sierpinski carpet. The fractal geometry proves that PDMS microfluidic channels produce the fine feature dimensions desired during graphene oxide deposit. Thermal reduction then reduces the graphene oxide into a purified state of graphene. Issues arise during thermal reduction because of excessive oxygen content in the furnace. The excess oxygen results in devices burning and additional oxidation of the gate contacts that prevents good electrical contact to the gates. Zero bias testing shows that the graphene oxide resistance decreases after thermal reduction, proving that thermal reduction of the devices occurs. Testing confirms a fabrication process producing graphene electronics; however, revision of processing steps, especially thermal reduction, should greatly improve the yield and functionality of the devices.