Maude es un lenguaje de programación declarativo basado en la lógica de reescritura que incorpora muchas características que lo hacen muy potente. Sin embargo, a la hora de probar ciertas propiedades computacionales esto conlleva dificultades. La tarea de probar la terminación de sistemas de reesctritura es de hecho bastante dura, pero aplicada a lenguajes de programación reales se concierte en más complicada debido a estas características inherentes. Esto provoca que métodos para probar la terminación de este tipo de programas requieran técnicas específicas y un análisis cuidadoso. Varios trabajos han intentado probar terminación de (un subconjunto de) programas Maude. Sin embargo, todos ellos siguen una aproximación transformacional, donde el programa original es trasformado hasta alcanzar un sistema de reescritura capaz de ser manejado con las técnicas y herramientas de terminación existentes. En la práctica, el hecho de transformar los sistemas originales suele complicar la demostración de la terminación ya que esto introduce nuevos símbolos y reglas en el sistema. En esta tesis, llevamos a cabo el problema de probar terminación de (un subconjunto de) programas Maude mediante métodos directos. Por un lado, nos centramos en la estrategia de Maude. Maude es un lenguaje impaciente donde los argumentos de una función son evaluados siempre antes de la aplicación de la función que los usa. Esta estrategia (conocida como llamada por valor) puede provocar la no terminación si los programas no están escritos cuidadosamente. Por esta razón, Maude (en concreto) incorpora mecanismos para controlar la ejecución de programas como las anotaciones sintácticas que están asociadas a los argumentos de los símbolos. En reescritura, esta estrategia sería conocida como reescritura sensible al contexto innermost (RSCI). Por otro lado, Maude también incorpora la posibilidad de declarar atributos. / Alarcón Jiménez, B. (2011). Towards a Framework for Proving Termination of Maude Programs [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11003 / Palancia

The purpose of this study was to identify the characteristics of cancer patients and the most frequently chosen nursing diagnoses, outcomes and interventions chosen for care plans from a large Midwestern acute care hospital. In addition the patients' outcome change scores and length of stay from the four oncology specialty units are investigated. Donabedian's structure-process-outcome model is the framework for this study. This is a descriptive retrospective study. The sample included a total of 2,237 patients admitted on four oncology units from June 1 to December 31, 2010. Data were retrieved from medical records, the nursing documentation system, and the tumor registry center. Demographics showed that 63% of the inpatients were female, 89% were white, 53 % were married and 26% were retired. Most patients returned home (82%); and 2% died in the hospital. Descriptive analysis identified that the most common nursing diagnoses for oncology inpatients were Acute Pain (78%), Risk for Infection (31%), and Nausea (26%). Each cancer patient had approximately 3.1 nursing diagnoses (SD=2.5), 6.3 nursing interventions (SD=5.1), and 3.7 nursing outcomes (SD=2.9). Characteristics of the patients were not found to be related to LOS (M=3.7) or outcome change scores for Pain Level among the patients with Acute Pain. Specifically, 88% of patients retained or improved outcome change scores. The most common linkage of NANDA-I, NOC, and NIC (NNN), a set of standardized nursing terminologies used in the study that represents nursing diagnoses, nursing-sensitive patient outcomes and nursing interventions, prospectively, was Acute Pain--Pain Level--Pain Management. Pain was the dominant concept in the nursing care provided to oncology patients. Risk for Infection was the most frequent nursing diagnosis in the Adult Leukemia and Bone Transplant Unit. Patients with both Acute Pain and Risk for Infection may differ among units; while the traditional study strategies rarely demonstrate this finding. Identifying the pattern of core diagnoses, interventions, and outcomes for oncology nurses can direct nursing care in clinical practice and provide direction for future research tot targets areas of high impact and guide education and evaluation of nurse competencies.

The present study explored whether use of Spanish language sayings, or dichos, improved group climate within multi-ethnic chronic pain groups. Use of this form of figurative language fits within psychological theory identifying use of metaphor as a means of promoting change and creating new meaning. Further, metaphor use is consistent with the broader aims of experiential therapy. Group climate was measured by group members' self reports using the Group Climate Questionnaire-Short Form. A pilot study involving Latino Americans in medical and non-medical contexts aided in categorizing dichos as high versus low-relevance. It was anticipated that clients would rate high-relevance sessions as involving greater engagement, and less conflict and avoidance than low-relevance groups. Participants were recruited from four multidisciplinary pain management clinics offering similar programs. Once every four to six weeks, group leaders were provided with a list of either high or low-relevance dichos, and were blind to the existence of dichos categories. Three hierarchical regression analyses were employed to determine whether dichos relevance, characterized as low, mixed or highly relevant, contributed to variance in group conflict, avoidance and engagement. Dichos familiarity was the last variable entered into the regression equation, with gender, ethnicity and acculturation score entered in sequential fashion. Consistent with predictions, low-relevance groups yielded higher conflict scores than all groups combined. Also, high-relevance groups predicted lower avoidance when compared to all groups. In contrast to hypotheses, high-relevance groups predicted lower ratings of group engagement when compared to all groups. Post-hoc analysis indicated the mixed-relevance groups yielded significantly higher engagement scores than the low and high-relevance groups. Implications of these findings are discussed in relation to impact on approaches to group therapy with Latino American clients, and within the chronic pain population. Limitations of the study and recommendations for future research are offered.

The performance of non-fullerene acceptor (NFA)- based organic solar cells (OSC) has shown continuous increase in recent years, reaching power-conversion efficiencies up to 17% through the design and synthesis of efficient acceptor materials. Recent research is directed towards achieving higher efficiency of OSC, which is limited by the open-circuit voltage (Voc) which is lower than the Voc values achieved in inorganic or perovskites solar cells with comparable bandgaps. In this work, voltage losses in NFA based OSC were calculated by investigating charge-transfer state energy (ECT) using electroluminescence spectroscopy and sensitive external quantum efficiency in three polymer:non-fullerene bulk heterojunction solar cells. PCE10:ITIC device acquired the highest ECT with a Voc of 0.82V, and a a power conversion efficiency (PCE) of 7.91%. While PCE10:O-IDTBR obtained the highest Voc of 1.03V, a PCE of 8.02% compared to PCE10:O-IDTBCN solar cell that has a lower Voc of 0.73V with a PCE of 7.98%. Both radiative and non-radiative voltage losses were calculated. In this thesis, the high open circuit voltage of PCE10:O-IDTBR is explained by the low non-radiative voltage losses compared to PCE10:O-IDTBCN and PCE10:ITIC devices.

Pubertal development is a time of growth and development in the brain, leading to high sensitivity during this period. Past research in our lab has shown that shipping female inbred and outbred mice during pubertal development alters their sensitivity to steroid hormones in adulthood, thus affecting sexual receptivity, cognition, depression-like behavior, and anxiety-like behavior. Here, we test the hypothesis that mice treated with lipopolysaccharide during pubertal development would have more active microglia, the brain's immune cells, after injection than mice treated with lipopolysaccharide in adulthood. No significant interactions were observed between treatment and age between any brain area measured, suggesting that pubertal development does not render the brain's immune system hypersensitive to environmental stressors.

No description available.

Cloud Gaming systems are among the most challenging networked-applications, since they deal with streaming high-quality and bulky video in real-time to players’ devices. While all industry solutions today are centralized, we introduce an AI-assisted hybrid networking architecture that, in addition to the central cloud servers, also uses some players’ computing resources as additional points of service. We describe the problem, its mathematical formulation, and potential solution strategy. Edge computing is a promising paradigm that brings servers closer to users, leading to lower latencies and enabling latency-sensitive applications such as cloud gaming, virtual/augmented reality, telepresence, and telecollaboration. Due to the high number of possible edge servers and incoming user requests, the optimum choice of user-server matching has become a difficult challenge, especially in the 5G era where the network can offer very low latencies. In this thesis, we introduce the problem of fair server selection as not only complying with an application's latency threshold but also reducing the variance of the latency among users in the same session. Due to the dynamic and rapidly evolving nature of such an environment and the capacity limitation of the servers, we propose as solution a Reinforcement Learning method in the form of a Quadruple Q-Learning model with action suppression, Q-value normalization, and a reward function that minimizes the variance of the latency. Our evaluations in the context of a cloud gaming application show that, compared to a existing methods, our proposed method not only better meets the application's latency threshold but is also more fair with a reduction of up to 35\% in the standard deviation of the latencies while using the geo-distance, and it shows improvements in fairness up to 18.7\% compared to existing solutions using the RTT delay especially during resource scarcity. Additionally, the RL solution can act as a heuristic algorithm even when it is not fully trained. While designing this solution, we also introduced action suppression, Quadruple Q-Learning, and normalization of the Q-values, leading to a more scalable and implementable RL system. We focus on algorithms for distributed applications and especially esports, but the principles we discuss apply to other domains and applications where fairness can be a crucial aspect to be optimized.

No description available.

With global emission regulations becoming stringent, development of new combustion technologies that meet future emission regulations is essential. In this vein, this dissertation presents the application of sensitive diagnostic tools to validate and improve chemical kinetic mechanisms that play a fundamental role in the design of new combustion technologies. First, a novel high sensitivity laser-based sensor with a wide frequency tuning range (900 – 1000 cm-1) was developed utilizing pulsed cavity ringdown spectroscopy (CRDS) technique. The novel laser-based sensor was illustrated by measuring trace amounts of multiple combustion intermediates, namely ethylene, propene, allene, and 1-butene in a static cell at ambient conditions. Subsequently, pulsed CRDS technique was utilized to develop an ultra-fast, high sensitivity diagnostic to monitor trace concentrations of ethylene in shock tube pyrolysis experiments. This diagnostic represented the first ever successful application of CRDS technique to transient species measurements in a shock tube. The high sensitivity and fast time response (10μs) diagnostic may be utilized for measuring other key neutrals and radicals which are crucial in the oxidation chemistry of practical fuels. Secondly, a quadrupole mass spectrometer (QMS) was employed to measure relative cation mole fractions in atmospheric and low-pressure (30 Torr) flames of methane/oxygen diluted in argon. Lean, stoichiometric and rich flames were 4 examined to evaluate the dependence of ion chemistry on flame stoichiometry. Spatial distribution of cations was compared with predictions of an existing ion chemistry model. Based on the extensive measurements carried out in this work, modifications were suggested to improve the ion chemistry model to enhance the fidelity of such mechanisms. In-depth understanding of flame ion chemistry is vital to model the interaction of flames with electric fields and thereby pave the way to enable active combustion control for increased efficiency and reduced emissions. Finally, a compact fast time-response time-of-flight mass spectrometer (TOFMS) was coupled to the shock tube through a pin-hole end-wall to enable timeresolved species concentration measurements. This diagnostic tool was demonstrated by investigating the decomposition of 1,3,5-trioxane over a wide range of shock conditions. Reaction rate coefficients were extracted by the best fit to the experimentally measured species time-histories. TOF-MS coupled to the shock tube is an ideal diagnostic tool for developing kinetic mechanisms for future fuels due to its ability to simultaneously measure several species during fuel pyrolysis/oxidation processes.

With the increase in energy consumption, new oil and gas extraction methods in unconventional resources have been explored. Hydraulic fracturing creates fractures to produce and make low permeability reservoirs economically profitable. Hydraulic fractures are also caused unintentionally by the uncontrolled injection in secondary recovery projects or CO2 geological storage. During proppant placement and CO2 injection, the permeability is reduced near the wellbore region due to pore clogging and mineral precipitation. The generated fractures act as high conductivity conduits that increase the capacity of flow in the reservoir. The fracture conductivity is strictly related to its geometry and hydraulic properties. However, these tend to degrade as pressure decreases. The current models do not consider fracture width change in the diffusivity inside the fracture. Additionally, the effect of fracture face skin in fracture closure has not been incorporated. This work focuses on the identification of fracture closure in fractured wells using Pressure Transient data. A semi-analytical model was developed for including the effects of fracture closure, fracture face skin, and complex fracture geometries. The matrix and fracture systems are coupled by pressure continuity at the interface. Fracture face skin is added, assuming a thin layer surrounding the fracture. The model is solved in Laplace space using a semi-analytical approach. The results are validated using a commercial simulator (CMG) and previous models. The pressure response in fractured wells with stress-sensitive fractures is analyzed at early, middle, and late times. In each time period, we identify pressure signals to detect fracture closure by incorporating effective fracture compressibility and fracture conductivity reduction. By incorporating the effective fracture compressibility, the model can reproduce a high storage capacity fracture signal. This signal occurs at early times and can help in post-fracture analysis. The fracture face skin creates an additional pressure drop in the fracture system, triggering conductivity reduction earlier than an undamaged fracture. We proposed a semi-log approach to identify fracture closure for slow rates of fracture closure and the pseudo-radial simplification to generate late time response curves instead of the complete solution for the model.