This thesis examines the concepts of textual order and chaos, and how Relevance Theory can be used to translate texts that do not adhere to conventional textual practices. Relevance Theory operates on the basis of presumed order in communication. Applying it to disordered communicative acts provides an opportunity and vocabulary to describe how communication can break down, and the consequences this can have for translation. This breakdown of order, which I am terming a ‘chaos principle’, will be examined through the lens of a Russian-language short story called “Lunokhod”, a story in which textual order, as described by Relevance Theory, breaks down. In this thesis, I first lay out several translation challenges presented by my corpus, discuss each with reference to Relevance Theory, and examine the implications for translation through sample translation segments. This deconstruction section argues that conventional translation methods fail to properly address the challenges of my corpus. Next comes a reconstruction section, in which I develop a theoretical framework for my translation that has roots in Relevance Theory but that frees the translation from the constraints imposed by an ordered view of communication. Finally, I present the translation itself.

Software complexity continues to accelerate, as new tools, frameworks, and technologiesbecome available. This, in turn, increases its fragility and liability. Despite the amount ofinvestment to test and harden their systems, companies still pay the price of failure. Towithstand this fast-paced development environment and ensure software availability, largescalesystems must be built with resilience in mind. Chaos Engineering is a new practicethat aims to assess some of these challenges. In this thesis, the methodology, requirements,and iterations of the system design and architecture for a chaos engineering tool arepresented. In a matter of only a couple of months and the working hours of two engineers, itwas possible to build a tool that is able to shed light on the attributes that make the targetedsystem resilient as well as the weaknesses in its failure handling mechanisms. This toolgreatly reduces the otherwise manual testing labor and allows software engineering teamsto find potentially costly failures. These results prove the benefits that many companiescould experience in their return of investment by adopting the practice of ChaosEngineering.

Health care providers in the United States expend more than $400 million in unnecessary direct costs annually managing the effects of widespread drug shortages. Based on the theory of complexity and complex adaptive systems, the purpose of this exploratory multiple case study was to identify the strategies that health care pharmaceutical procurement leaders from the Eastern region of the United States use to address widespread drug shortages. Data were collected from 5 semistructured interviews with pharmaceutical procurement leaders, recorded field notes, and a review of public documents from company websites. Data analysis included deductive and open coding techniques. Emergent themes included: (a) proactive planning for supply chain and distribution channel disruptions, (b) creating strategic processes for alternative procurement methods, and (c) relying on proven sources of actionable information. Findings may influence business practices for health care procurement leaders by contributing new knowledge to develop strategies to address disruptions and drug shortages. Health care policy makers may use the findings to assess key strategies in delivering pharmaceutical products from manufacturers to end users.

The response to civil disturbances has historically been the aggressive use of force or escalation with tactics such as the use of police dogs, armed federal troops during war protests, and police field forces. These types of tactics can escalate tensions between protestors and police and only add to the violence and destruction of the incident. To reduce the violence between protestors and the police and the destruction often associated with civil disturbances, it is necessary to examine the need to include de-escalation techniques in the responses. This study utilized 3 theoretical frameworks, the chaos theory, the behavioral decision theory and the strain theory, all which complement each other in interpreting the opinions and experiences of participants and civil disturbance responses. The research questions were used to determine the influence of experience, training, personal biases or external influences on decision making and elicit the opinions of respondents in how they would respond to a civil disturbance. Twenty-five respondents responsible for policy or response decisions regarding civil disturbances from southern U.S. state emergency management and law enforcement agencies took part in the survey. The results of a cross-tabulation analysis determined that there is a need for the inclusion of de-escalation techniques and that they would be effective in civil disturbances. The results also showed that an aggressive response was the preferred method to restoring or maintaining order, but there was a need to examine changes in response tactics. This study may be beneficial and provide a social impact through policy changes, which may lead to a lessening of the severity and scope of an incident.

In chaos theory there are many different problems still unsolved. One of which is the optimization of infinite time average functionals on manifolds. To try one of the different tools to solve this problem we want to find stable manifolds in chaotic dynamical systems.In this thesis we find different manifolds for the Lorenz system when using a time dependent $\mu$ parameter and perform a sensitivity analysis on some of them. The existence of these manifolds are motivated numerically with the help of the shadowing lemma and extensive comparison of different numerical solvers.

No description available.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第24412号 / 理博第4911号 / 新制||理||1702(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 橋本 幸士, 准教授 福間 將文, 教授 杉本 茂樹 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

The viability of a drug delivery system which encapsulates chemotherapeutic drugs (Doxorubicin) in the hydrophobic core of polymeric micelles and triggers release by ultrasound application was investigated at an applied frequency of 500 kHz. The investigation also included elucidating the mechanism of drug release at 70 kHz, a frequency which had previously been shown to induce drug release. A fluorescence detection chamber was used to measure in vitro drug release from both Pluronic and stabilized micelles and a hydrophone was used to monitor bubble activity during the experiments. A threshold for release between 0.35 and 0.40 in mechanical index was found at 70 kHz and shown to correspond with the appearance of the subharmonic signal in the acoustic spectrum. Additionally, drug release was found to correlate with increase in subharmonic emission. No evidence of drug release or of the subharmonic signal was detected at 500 kHz. These findings confirmed the role of cavitation in ultrasonic drug release from micelles. A mathematical model of a bubble oscillator was solved to explore the differences in the behavior of a single 10 um bubble under 70 and 500 kHz ultrasound. The dynamics were found to be fundamentally different; the bubble follows a period-doubling route to chaos at 500 kHz and an intermittent route to chaos at 70 kHz. It was concluded that this type of "intermittent subharmonic" oscillation is associated with the apparent drug release. This research confirmed the central role of cavitation in ultrasonically-triggered drug delivery from micelles, established the importance of subharmonic bubble oscillations as an indicator, and expounded the key dynamic differences between 70 and 500 kHz ultrasonic cavitation.

Fractional diffusion equations have played an increasingly important role in ex- plaining long-range interactions, nonlocal dynamics and anomalous diffusion, pro- viding effective means of describing the memory and hereditary properties of such processes. This dissertation explores the uncertainty propagation in space fractional diffusion equations in one and multiple dimensions with variable diffusivity and order parameters. This is achieved by:(i) deploying accurate numerical schemes of the forward problem, and (ii) employing uncertainty quantifications tools that accelerate the inverse problem. We begin by focusing on parameter calibration of a variable- diffusivity fractional diffusion model. A random, spatially-varying diffusivity field is considered together with an uncertain but spatially homogeneous fractional operator order. Polynomial chaos (PC) techniques are used to express the dependence of the stochastic solution on these random variables. A non-intrusive methodology is used, and a deterministic finite-difference solver of the fractional diffusion model is utilized for this purpose. The surrogates are first used to assess the sensitivity of quantities of interest (QoIs) to uncertain inputs and to examine their statistics. In particular, the analysis indicates that the fractional order has a dominant effect on the variance of the QoIs considered. The PC surrogates are further exploited to calibrate the uncertain parameters using a Bayesian methodology. In the broad range of parameters addressed, the analysis shows that the uncertain parameters having a significant impact on the variance of the solution can be reliably inferred, even from limited observations. Next, we address the numerical challenges when multidimensional space-fractional diffusion equations have spatially varying diffusivity and fractional order. Significant computational challenges arise due to the kernel singularity in the fractional integral operator as well as the resulting dense discretized operators. Hence, we present a singularity-aware discretization scheme that regularizes the singular integrals through a singularity subtraction technique adapted to the spatial variability of diffusivity and fractional order. This regularization strategy is conveniently formulated as a sparse matrix correction that is added to the dense operator, and is applicable to different formulations of fractional diffusion equations. Numerical results show that the singularity treatment is robust, substantially reduces discretization errors, and attains the first-order convergence rate allowed by the regularity of the solutions. In the last part, we explore the application of a Bayesian formalism to detect an anomaly in a fractional medium. Specifically, a computational method is presented for inferring the location and properties of an inclusion inside a two-dimensional domain. The anomaly is assumed to have known shape, but unknown diffusivity and fractional order parameters, and is assumed to be embedded in a fractional medium of known fractional properties. To detect the presence of the anomaly, the medium is forced using a collection of localized sources, and its response is measured at the source locations. To this end, the singularity-aware finite-difference scheme is applied. A non-intrusive regression approach is used to explore the dependence of the computed signals on the properties of the anomaly, and the resulting surrogates are first exploited to characterize the variability of the response, and then used to accelerate the Bayesian inference of the anomaly. In the regime of parameters considered, the computational results indicate that robust estimates of the location and fractional properties of the anomaly can be obtained, and that these estimates become sharper when high contrast ratios prevail between the anomaly and the surrounding matrix.

IoT (Internet of Things) systems implement event-driven architectures that are deployed on an ever-increasing scale as more and more devices (things) become connected to the internet. Consequently, IoT cloud platforms are becoming increasingly distributed and complex as they adapt to handle larger amounts of user requests and device data. The complexity of such systems makes it close to impossible to predict how they will handle failures that inevitably occur once they are put into production. Chaos engineering, the practice of deliberately injecting faults in production, has successfully been used by many software companies as a means to build confidence in that their complex systems are reliable for the end-users. Nevertheless, its applications in the scope of IoT systems remain largely unexplored in research. Modern IoT cloud platforms are built cloud native with containerized microservices, container orchestration, and other cloud native technologies, much like any other distributed cloud computing system. We therefore investigate cloud native chaos engineering technology and its applications in IoT cloud platforms. We also introduce a framework for getting started with using cloud native chaos engineering to verify and improve the resilience of IoT systems and evaluate it through a case study at a commercial home appliance manufacturer. The evaluation successfully reveals unknown system behavior and results in the discovery of potential resilience improvements for the case study IoT system. The evaluation also shows three ways to measure the resilience of IoT cloud platforms with respect to perturbations, these are: (1) success rate of user requests, (2) system health, and (3) event traffic. / IoT(Sakernas Internet)-system implementerar händelsedrivna arkitekturer som driftsätts i allt större skala i och med att allt fler enheter (saker) blir anslutna till internet. IoT-molnplattformar blir därmed alltmer distribuerade och komplexa i takt med att de anpassas till att hantera större mängder användarförfrågningar och enhetsdata. Komplexiteten hos sådana system gör det nära omöjligt att förutsäga hur de hanterar problem som oundvikligen inträffar när de väl körs i produktionsmiljö. Kaosteknik, att avsiktligt injicera fel medans ett system körs i produktionsmiljön, har framgångsrikt använts av många mjukvaruföretag som ett sätt att bygga förtroende för att deras komplexa system är tillförlitliga för slutanvändarna. Trots det är dess tillämpningar inom ramen för IoT-system i stort sett outforskade inom dataforskning. Moderna IoT-molnplattformar byggs molnäkta med containeriserade mikrotjänster, containerorkestering, och andra molnäkta teknologier, precis som andra distribuerade molntjänstsystem. Vi undersöker därför molnäkta kaosteknik och dess tillämpningar i IoT-molnplattformar. Vi introducerar även ett ramverk för att komma igång med att använda molnäkta kaosteknik för att verifiera och förbättra motståndskraften hos IoT-system och utvärderar det genom en fallstudie hos en kommersiell tillverkare av hushållsapparater. Utvärderingen lyckas avslöja okänt systembeteende och resulterar i upptäckten av potentiella motståndskraftsförbättringar för IoT-systemet i fallstudien. Utvärderingen visar också tre sätt att mäta motståndskraften hos IoT-molnplattformar med hänsyn till störningar, dessa är: (1) andel framgångsrika användarförfrågningar, (2) systemhälsa och (3) händelsetrafik.