Euclid's geometry is well-known for its theorems concerning triangles and circles. Less popular are the contents of the tenth book, in which geometry is a means to study quantity in general. Commensurability and rational quantities are first principles, and from them are derived at least eight species of irrationals. A recently republished work by Johannes Kepler contains examples using polygons to illustrate these species. In addition, figures having these quantities in their construction form solid shapes (polyhedra) having origins though Platonic philosophy and Archimedean works. Kepler gives two additional polyhedra, and a simple means for constructing the “divine” proportion is given.

Esta investigación tiene como propósito identificar cuáles son los factores más determinantes para un posible comprador de un auto híbrido en comparación a un auto convencional en Lima Metropolitana, es por esto que se han utilizado herramientas cualitativas y cuantitativas para obtener un resultado; esto permitirá a las marcas de autos saber que factor es más importante para el consumidor y potenciarlo a través de estrategias de marketing directo e indirecto. En el análisis cualitativo desarrollado a través de un focus group se encontraron diferentes afirmaciones y negaciones que son respaldadas a través del análisis cuantitativo con el modelo estadístico logístico binario; en este caso se demostró que todas las variables son importantes para un posible comprador de un auto híbrido; pero unas tienen mayor importancia que otras y esto determinaría que se deben llevar a cabo estrategias que estén relacionadas al precio y diseño del auto; ya que son las variables que obtuvieron mayor importancia en el análisis. / This investigation has as purpose identify which are the factors more important for a possible buyer of a hybrid car in comparison of a conventional car in Lima Metropolitana, that is why qualitative and quantitative tools have been used to get a result; this will allow car brands to know which factor is more important for the consumer and to empower it through direct and indirect marketing strategies. In the qualitative analysis developed through a focus group, different affirmations and negations were found that are supported through quantitative analysis with the statistical binary logistic model; in this case it was shown that all the variables are important for a possible buyer of a hybrid car; but some are more important than others and this would determine that price and design should be the ones that have to have the focus of the strategies because they are the variables that obtained more importance in the statistical analysis. / Tesis

Transgender individuals challenge the binary definition of gender accepted in Pakistani society and as a result, Pakistan’s transgender community is highly marginalized. Pakistani society regards transgender individuals as “abnormal,” because their physical appearance and behavior fail to conform with conventional expectations. Based on contextual research and interviews conducted with transgender individuals in Pakistan, my thesis explores the obstacles transgender individuals face in the course of everyday survival. My research responds to the physical realities of being transgender in Pakistani society and analyzes cultural norms associated with gender, which trigger harassment. By designing transformable apparel for these individuals, informed by primary and secondary research, my goal is to help them cope with the everyday struggles of being transgender in Pakistan.

Les mélanges de toluène et de tert-butanol sont complètement miscibles dans toutes les compositions à l'échelle macroscopique. Toutefois tert-butanol forme un réseau de liaisons hydrogène à l'échelle nanométrique qui persistent même dans les mélanges liquides binaires tert-butanol/toluène. Des expériences de diffusion de neutrons ont révélé la séparation des phases du mélange dans une structure cœur-gaine sous confinement dans des solides nanoporeux hydrophiles. Le travail effectué dans cette thèse vise à comprendre le rôle joué par la concurrence des interactions intermoléculaires (liaisons hydrogène, Van der Waals) lors de la séparation de phase sous nanoconfinement. Des expériences de RMN révèlent la persistance d'un réseau de liaisons hydrogène dans ces liquides binaires confinés dans des nanopores de silice à des concentrations faibles de tert -butanol, fournissant la preuves d'un autre type de réseau de liaisons hydrogène sous confinement. Des expériences d'adsorption du mélange gazeux dans des nanopores de silice ont aidé à expliquer l'affinité plus élevée des parois de silice polaires pour tert-butanol par une modèle thermodynamique. Le remplacement de la matrice hôte par un analogue hydrophobe est associé à une inversion de la sélectivité, montrant cette fois une plus grande affinité du toluène pour la surface des pores. L'effet des interactions spécifiques avec la surface sur la dynamique d'imbibition spontanée de ces liquides binaires à travers le réseau de silice nanoporeux a également été étudié. Des expériences de radiographie de neutrons ont révélé la séparation des flux à deux composants, au sein d'une dynamique toujours gouvernée par une loi de Lucas-Washburn. / Toluene and tert-butanol mixtures are completely miscible for all compositions at the macroscopic scale. However tert-butanol forms a network of hydrogen-bonded clusters at the nanoscale which persist even in the tert-butanol-toluene binary liquid mixtures. Interpretation of neutron scattering experiments revealed phase separation of the mixture into a core-shell structure inside hydrophilic nanoporous solids, with a tert-butanol shell and a toluene core. The work carried out in this thesis is aimed at understanding the role played by competing intermolecular interactions (hydrogen-bonding, van der Waals) in driving phase separation in confinement. NMR experiments reveal the persistence of a hydrogen-bonding network in these binary liquids confined in silica nanopores even at very low concentrations of tert-butanol, providing evidence of a new kind of hydrogen bonded network under confinement. Vapour sorption isotherms of tert-butanol-toluene binary gas mixtures in silica nanopores helped explain higher affinity of polar silica walls for tert-butanol by a thermodynamic model. Replacing the host matrix by a hydrophobic analogue was found to reverse the selectivity, with toluene showing greater affinity for the pore surface. Effect of surface specific interactions was studied on spontaneous imbibition dynamics of these binary liquids through nanoporous silica network. Neutron radiography experiments revealed the separation of fluxes into a twocomponent flow, generally obeying the Lucas-Washburn law.

Measurements are carried out for dendrite tip growth of succinonitrile-acetone alloys solidifying freely in an undercooled melt. The current experimental investigation is conducted using the equiaxed dendritic solidification experiment (EDSE). This setup allows for precise measurements of the dendrite tip velocity, radius and shape for a range of undercoolings and solute concentrations. The collected data are compared to available theories of free dendritic growth, such as the Lipton-Glicksman-Kurz and Li-Beckermann models. It is found that for dilute succinonitrile-acetone alloys, the measured dendrite tip Péclet numbers agree well with previous theories of free dendritic growth, if the effects of melt convection are taken into account. The tip selection parameter deviates significantly from the pure succinonitrile value and is inversely related to the applied undercooling. Besides, the selection parameter shows no dependence on the solute concentration. These results are consistent with phase-field simulations and preceding experimental investigations. In addition, scaling relationships for the sidebranching shape were obtained in terms of the dendritic envelope, projection area and contour length. These new scaling relations agree well with previous measurements in pure succinonitrile dendrites by Li and Beckermann.

In the survival and reliability data analysis, parametric and nonparametric methods are used to estimate the hazard/risk rate and survival functions. A parametric approach is based on the assumption that the underlying survival distribution belongs to some specific family of closed form distributions (normal, Weibull, exponential, etc.). On the other hand, a nonparametric approach is centered around the best-fitting member of a class of survival distribution functions. Moreover, the Kaplan-Meier and Nelson-Aalen type nonparametric approach do not assume either distribution class or closed-form distributions. Historically, well-known time-to-event processes are death of living specie in populations and failure of component in engineering systems. Recently, the human mobility, electronic communications, technological changes, advancements in engineering, medical, and social sciences have further diversified the role and scope of time-to-event processes in cultural, epidemiological, financial, military, and social sciences. To incorporate extensions, generalizations and minimize scope of existing methods, we initiate an innovative alternative modeling approach for time-to-event dynamic processes. The innovative approach is composed of the following basic components: (1) development of continuous-time state of dynamic process, (2) introduction of discrete-time dynamic intervention process, (3) formulation of continuous and discrete-time interconnected dynamic system, (4) utilizing Euler-type discretized schemes, developing theoretical dynamic algorithms, and (5) introduction of conceptual and computational state and parameter estimation procedures. The presented approach is motivated by state and parameter estimation of time-to-event processes in biological, chemical, engineering, epidemiological, medical, military, multiple-markets and social dynamic processes under the influence of discrete-time intervention processes. We initiate (1) a time-to-event process to be a probabilistic dynamic process with unitary state. Action, normal, operational, radical, survival, susceptible, etc. and its complementary states, reaction, abnormal, nonoperational, non-radical, failure, infective and so on (quantitative and qualitative variables), are considered to be illustrations of a unitary state of time-to-event dynamic processes. A unitary state is measured by a probability distribution function. Employing Newtonian dynamic modeling approach and observing the definition of hazard rate as a specific rate, survival or failure probabilistic state dynamic model is developed. This dynamic model is further extended to incorporate internal or external discrete-time dynamic intervention processes acting on unitary state time-to-event processes (2). This further demanded a formulation and development of an interconnected continuous-discrete-time hybrid, and totally discrete-time dynamic models for time-to-event processes (3). Employing the developed hybrid model, Euler-type discretized schemes, a very general fundamental conceptual analytic algorithm is outlined (4). Using the developed theoretical computational procedure in (4), a general conceptual computational data organizational and simulation schemes are presented (5) for state and parameter estimation problems in unitary state time-to-event dynamic processes. The well-known theoretical existing results in the literature are exhibited as special cases in a systematic and unified manner (6). In fact, the Kaplan-Meier and Nelson-Aalen type nonparametric estimation approaches are systematically analyzed by the developed totally discrete-time hybrid dynamic modeling process. The developed approach is applied to two data sets. Moreover, this approach does not require a knowledge of either a closed-form solution distribution or a class of distributions functions. A hazard rate need not be constant. The procedure is dynamic. In the existing literature, the failure and survival distribution functions are treated to be evolving/progressing mutually exclusively with respect to corresponding to two mutually exclusive time varying events. We refer to these two functions (failure and survival) as cumulative distributions of two mutually disjoint state output processes with respect to two mutually exclusive time-varying complementary unitary states of a time-to-event processes in any discipline of interest (7). This kind of time-to-event process can be thought of as a Bernoulli-type of deterministic/stochastic process. Corresponding to these two complementary output processes of the Bernoulli-type of stochastic process, we associate two unitary dynamic states corresponding to a binary choice options/actions (8), namely, ({action, reaction}, {normal, abnormal}, {survival, failure}, {susceptible, infective}, {operational, nonoperational}, {radical, non-radical}, and so on.) Under this consideration, we extend unitary state time-to-event dynamic model to binary state time-to-event dynamic model. Using basic tools in mathematical sciences, we initiate a Newtonian-type dynamic approach for binary state time-to-event processes in the sciences, technologies, and engineering (9). Introducing an innovative concept of “survival state dynamic principle”, an innovative interconnected nonlinear non-stationary large-scale hybrid dynamic model for number of units/species and its unitary survival state corresponding to binary state time-to-event process is formulated (10). The developed model in (10) includes dynamic model (3) as a special case. The developed approach is directly applicable to binary state time-to-event dynamic processes in biological, chemical, engineering, financial, medical, physical, military, and social sciences in a coherent manner. A by-product of this is a transformed interconnected nonlinear hybrid dynamic model with a theoretical discrete-time conceptual computational dynamic process (11). Employing the transformed discrete-time conceptual computational dynamic process, we introduce notions of data coordination, state data decomposition and aggregation, theoretical conceptual iterative processes, conceptual and computational parameter estimation and simulation schemes, conceptual and computational state simulation schemes in a systematic way (12). The usefulness of the developed interconnected algorithm is validated by using three real world data sets (13). We note that the presented algorithm does not need a closed-form representation of distribution/likelihood function. In fact, it is free from any required assumptions of the “Classical Maximum Likelihood Function Approach” in the “Survival and Reliability Analysis.” The rapid electronic communication and human mobility processes have facilitated to transform information, knowledge, and ideas almost instantly around the globe. This indeed generates heterogeneity, and it causes to form nonlinear and non-stationary dynamic processes. Moreover, the heterogeneity, non-linearity, non-stationarity, further generates two types of uncertainties, namely, deterministic, and stochastic. In view of this, it is obvious that nothing is deterministic. In short, the 21st century problems are highly nonlinear, non-stationary and under the influence of internal and external random perturbations. Using tools in stochastic analysis, interconnected deterministic models in (3) and (10) are extended to interconnected stochastic hybrid dynamic model for binary state time-to-event processes (14). The developed model is described by a large-scale nonlinear and non-stationary stochastic differential equations. Moreover, a stochastic version of a survival function is also introduced (15). Analytical, computational, statistical, and simulation algorithms/procedures are also extended and analyzed in a systematic and unified way (16). The presented interconnected stochastic model is motivated to initiate conceptual computational parameter and state estimation schemes for time-to-event statistical data (17). Again, stochastic version of computational algorithms are validated in the context of three real world data sets. The obtained parameter and state estimates show that the algorithm is independent of the choice of nonlinear transformation (18). Utilizing the developed alternative innovative procedure and the recently modified deterministic version of Local Lagged Adapted Generalized Method of Moments (LLGMM) is also extended to stochastic version in a natural way (19). This approach provides a degree of measure of confidence, prediction, and planning assessments (20). In addition, it initiates a conceptual computational parameter and state estimation and simulation schemes that is suitable for the usage of mean square sub-optimal procedure (21). The usefulness and the significance of the approach is illustrated by applying to three data sets (22). The approach provides insight for investigating various type of invariant sets, namely, sustainable/unsustainable, survival/failure, reliable/unreliable (23), and qualitative properties such as sustainability versus unsustainability, reliability versus unreliability, etc. (24) Once again, the presented algorithm is independent of any form of survival distribution functions or data sets. Moreover, it does not require a closed form survival function distribution. We also note that the introduction of intervention processes provides a measure of influence and confidence for the usage of new tools/procedures/approaches in continuous-time binary state time-to-event dynamic process (25). Moreover, the presented dynamic modeling is more feasible for its usage of investigating a more complex time-to-event dynamic process (26). The developed procedure is dynamic and indeed non-parametric (27). The dynamic approach adapts with current changes and updates statistic process (28). The dynamic nature is natural rather than the existing static and single-shot techniques (29).

Malware analysis and detection continues to be one of the central battlefields for cybersecurity industry. For the desktop malware domain, we observed multiple significant ransomware attacks in the past several years, e.g., it was estimated that in 2017 the WannaCry ransomware attack affected more than 200,000 computers across 150 countries with hundreds of millions damages. Similarly, we witnessed the increased impacts of Android malware on global individuals due to the popular smartphone and IoT devices worldwide. In this dissertation, we describe similarity comparison based novel techniques that can be applied to achieve large scale desktop and Android malware analysis, and the practical implications of machine learning based approaches for malware detection. First, we propose a generic and effective solution for accurate and efficient binary similarity analysis of desktop malware. Binary similarity analysis is an essential technique for a variety of security analysis tasks, including malware detection and malware clustering. Even though various solutions have been developed, existing binary similarity analysis methods still suffer from limited efficiency, accuracy, and usability. In this work, we propose a novel graphical fuzzy hashing scheme for accurate and efficient binary similarity analysis. We first abstract control flow graphs (CFGs) of binary codes to extract blended n-gram graphical features of the CFGs, and then encode the graphical features into numeric vectors (called graph signatures) to measure similarity by comparing the graph signatures. We further leverage a fuzzy hashing technique to convert the numeric graph signatures into smaller fixed size fuzzy hash outputs for efficient comparisons. Our comprehensive evaluation demonstrates that our blended n-gram graphical feature based CFG comparison is more effective and efficient compared to existing CFG comparison techniques. Based on our CFG comparison method, we develop BingSim, a binary similarity analysis tool, and show that BingSim outperforms existing binary similarity analysis tools while conducting similarity analysis based malware detection and malware clustering. Second, we identify the challenges faced by overall similarity based Android malware clustering and design a specialized system for solving the problems. Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party libraries in Android application development and the widespread use of repackaging in malware development. We design and implement an Android malware clustering system through iterative mining of malicious payloads and checking whether malware samples share the same version of malicious payloads. Our system utilizes a hierarchical clustering technique and an efficient bit-vector format to represent Android apps. Experimental results demonstrate that our clustering approach achieves precision of 0.90 and recall of 0.75 for the Android Genome mal- ware dataset, and average precision of 0.98 and recall of 0.96 with respect to manually verified ground-truth. Third, we study the fundamental issues faced by traditional machine learning (ML) based Android malware detection systems, and examine the role of ML for Android malware detection in practice, which leads to a revised evaluation strategy that evaluates an ML based malware detection system by checking their zero-day detection capabilities. Existing machine learning based Android malware research obtains the ground truth by consulting AV products, and uses the same label set for training and testing. However, there is a mismatch between how the ML system has been evaluated, and the true purpose of using ML system in practice. The goal of applying ML is not to reproduce or verify the same potentially imperfect knowledge, but rather to produce something that is better — closer to the ultimate ground truth about the apps’ maliciousness. Therefore, it will be more meaningful to check their zero-day detection capabilities than detection accuracy for known malware. This evaluation strategy is aligned with how an ML algorithm can potentially benefit malware detection in practice, by acknowledging that any ML classifier has to be trained on imperfect knowledge, and such knowledge evolves over time. Besides the traditional malware prediction approaches, we also examine the mislabel identification approaches. Through extensive experiments, we demonstrate that: (a) it is feasible to evaluate ML based Android malware detection systems with regard to their zero-day malware detection capabilities; (b) both malware prediction and mislabel identification approaches can be used to achieve verifiable zero-day malware detection, even when trained with an old and noisy ground truth dataset.

A eficiência de motores a combustão está diretamente relacionada a mistura dos reagentes. O que é muito desejado em todos os sistemas, inclusive em sistemas aeroespaciais onde a combustão ocorre de forma contínua como nos motores a jato, motores de foguete, ramjet e scramjet. No caso do scramjet a combustão ocorre em regime supersônico e conhecer como os dois fluidos se misturam na câmera de combustão é muito importante pois o tempo de residência na câmera é muito reduzido, aumentando a importância de uma mistura homogênea para a eficiência da combustão. Em determinados casos pequenas pertubações em um jato vão se amplificar podendo ocasionar a transição do escoamento laminar para turbulento. O que é desejado visto que escoamentos turbulentos são caracterizados pela grande capacidade de mistura. No presente trabalho estudamos a estabilidade de jatos coaxiais composto por dois fluídos com pequenas pertubações na base para escoamentos supersônicos para casos bidimensionais e tridimensionais. O escoamento foi investigado utilizando simulação numérica e a teoria de estabilidade linear, os resultados de ambos os métodos foram comparados, casos com pertubações bidimencionais e tridimencionais foram analizados. As simulações numéricas foram realizadas utilizando diferenças finitas de alta ordem de precisão para a discretização espacial. A integração temporal foi feita utilizando o método de Runge-Kutta de quarta ordem. Os resultados de ambos os métodos mostraram uma boa concordância. / The efficiency of combustion engines are strict related to the mixing between reagents. That is very desire in every aerospace propulsion system, when the combustion is continuous such as rocket engines, ramjets and scramjets. The combustion in scramjet occurs in supersonic speed ith a very small resilience time, know how two fluids are mixed in this case is very important because are direct related to the efficiency of the combustion. In such cases small disturbances in a jet flow can be amplified until occur the transition from a laminar flow to a turbulent flow, that is desired because the great capacity of mixing of turbulent flows. In this work are studied the jet flow stability when the jet are composed by two fluids with small disturbances in the base for supersonic flows in bidimensional and tridimensional cases. The numerical results are obtained by numerical simulation and linear stability theory (LST). High order finite difference schemes are adopted for spatial derivatives. The integration in time are caried out by a fouth order Runge-Kutta scheme. The results obtained by numerical simulation and linear stability theory show good agreement.

Real graph datasets are important for many science domains, from understanding epidemics to modeling traffic congestion. To facilitate access to realistic graph datasets, researchers proposed various graph generators typically aimed at representing particular graph properties. While many such graph generators exist, there are few techniques for generating graphs where the nodes have binary attributes. Moreover, generating such graphs in which the distribution of the node attributes preserves real-world characteristics is still an open challenge. This thesis introduces Borromean, a graph generating algorithm that creates synthetic graphs with binary node attributes in which the attributes obey an attribute-specific joint degree distribution. We show experimentally the accuracy of the generated graphs in terms of graph size, distribution of attributes, and distance from the original joint degree distribution. We also designed a parallel version of Borromean in order to generate larger graphs and show its performance. Our experiments show that Borromean can generate graphs of hundreds of thousands of nodes in under 30 minutes, and these graphs preserve the distribution of binary node attributes within 40% on average.

The environment is made up of composition of small particles. Hence, particle simulation is an important tool in many scientific and engineering research fields to simulate the real life processes of the environment. Because of the enormous amount of data in such simulations, data management, storage and processing are very challenging tasks. Spatial Distance Histogram (SDH) is one of the most popular queries being used in this field. In this thesis, we are interested in investigating the performance of improvement of an existing algorithm for computing SDH. The algorithm already being used is using a conceptual data structure called density map which is implemented via a quad tree index. An algorithm having density maps implemented via binary tree is proposed in this thesis. After carrying out many experiments and analysis of the data, we figure out that although the binary tree approach seems efficient in earlier stage, it is same as the quad tree approach in terms of time complexity. However, it provides an improvement in computing time by a constant factor for some data inputs. The second part of this thesis is dedicated to an approach that can potentially reduce the computational time to a great extent by taking advantage of regions where data points are uniformly distributed.