Increased genome instability in Escherichia coli lon mutants: relation to emergence of multiple-antibiotic-resistant (Mar) mutants caused by insertion sequence elements and large tandem genomic amplifications

Thirteen spontaneous multiple-antibiotic-resistant (Mar) mutants of Escherichia coli AG100 were isolated on Luria-Bertani (LB) agar in the presence of tetracycline (4 microg/ml). The phenotype was linked to insertion sequence (IS) insertions in marR or acrR or unstable large tandem genomic amplifications which included acrAB and which were bordered by IS3 or IS5 sequences. Five different lon mutations, not related to the Mar phenotype, were also found in 12 of the 13 mutants. Under specific selective conditions, most drug-resistant mutants appearing late on the selective plates evolved from a subpopulation of AG100 with lon mutations. That the lon locus was involved in the evolution to low levels of multidrug resistance was supported by the following findings: (i) AG100 grown in LB broth had an important spontaneous subpopulation (about 3.7x10(-4)) of lon::IS186 mutants, (ii) new lon mutants appeared during the selection on antibiotic-containing agar plates, (iii) lon mutants could slowly grow in the presence of low amounts (about 2x MIC of the wild type) of chloramphenicol or tetracycline, and (iv) a lon mutation conferred a mutator phenotype which increased IS transposition and genome rearrangements. The association between lon mutations and mutations causing the Mar phenotype was dependent on the medium (LB versus MacConkey medium) and the antibiotic used for the selection. A previously reported unstable amplifiable high-level resistance observed after the prolonged growth of Mar mutants in a low concentration of tetracycline or chloramphenicol can be explained by genomic amplification.

Effect of microstructure and alloying elements on the resistance of fastener grade steels to hydrogen assisted cracking

Fastener grade steels with varying alloy contents and heat treatments were employed to measure changes in resistance to hydrogen assisted cracking. The testing procedure compared notched tension specimens fractured in air to threshold stress values obtained during hydrogen charging, utilizing a rising step load procedure. Bainitic structures improved resistance by 10-20% compared to tempered martensite structures. Dual phase steels with a tempered martensite matrix and 20% ferrite were more susceptible and notch sensitive. High strength, fully pearlitic structures showed an improvement in resistance. Carbon content, per se, had no effect on the resistance of steel to hydrogen assisted cracking. Chromium caused a deleterious effect but all other alloying elements studied did not cause much change in hydrogen assisted cracking susceptibility.

DC MICROGRID STABILIZATION THROUGH FUZZY CONTROL OF INTERLEAVED, HETEROGENEOUS STORAGE ELEMENTS

As microgrid power systems gain prevalence and renewable energy comprises greater and greater portions of distributed generation, energy storage becomes important to offset the higher variance of renewable energy sources and maximize their usefulness. One of the emerging techniques is to utilize a combination of lead-acid batteries and ultracapacitors to provide both short and long-term stabilization to microgrid systems. The different energy and power characteristics of batteries and ultracapacitors imply that they ought to be utilized in different ways. Traditional linear controls can use these energy storage systems to stabilize a power grid, but cannot effect more complex interactions. This research explores a fuzzy logic approach to microgrid stabilization. The ability of a fuzzy logic controller to regulate a dc bus in the presence of source and load fluctuations, in a manner comparable to traditional linear control systems, is explored and demonstrated. Furthermore, the expanded capabilities (such as storage balancing, self-protection, and battery optimization) of a fuzzy logic system over a traditional linear control system are shown. System simulation results are presented and validated through hardware-based experiments. These experiments confirm the capabilities of the fuzzy logic control system to regulate bus voltage, balance storage elements, optimize battery usage, and effect self-protection.

MODEL UPDATING AND STRUCTURAL HEALTH MONITORING OF HORIZONTAL AXIS WIND TURBINES VIA ADVANCED SPINNING FINITE ELEMENTS AND STOCHASTIC SUBSPACE IDENTIFICATION METHODS

Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.