<div>In recent years, intelligent devices such as smartphones and self-driving cars are becoming ubiquitous in daily life, and thus, wireless communication is turning out to be increasingly omnipresent. To efficiently utilize the electromagnetic spectrum, automatically reconfigurable software-controlled radio transceivers are drawing an extensive amount of attention. In order to implement a reconfigurable radio transceiver, automatically tunable RF front-end components such as tunable filters are indispensable. Over the last decade, tunable filters have shown promising performance with high-quality factor (Q), a wide tuning range, and high-power handling. However, most of the existing tunable filters are manually adjusted. In this regard, this research work focuses on developing a novel automatic software-driven tuning technique for continuously tunable microwave and millimeter-wave filters.</div><div><br></div><div><br></div><div>First, a K-band continuously tunable bandpass filter has been demonstrated with contactless printed circuit board (PCB) tuners. Then, an automatic tuning technique based on deep-Q learning has been proposed and realized to tune a filter with contactless tuners automatically. Two-pole, three-pole, and four-pole bandpass filters are experimentally tested as examples without any human intervention to prove the feasibility of the tuning technique. For the first time, unlike a look-up table, the filters can be continuously tuned at a practically infinite number of frequencies inside the tuning range. </div><div><br></div><div>Next, a K/Ka-band tunable absorptive bandstop filter (ABSF) has been designed and fabricated in low-cost PCB technology. Contrary to a reflective bandstop filter, an ABSF filter is preferred for interference mitigation due to its deeper notch and lower reflection. However, the absorbed power may limit the filter's power handling. Therefore, lastly, a comparative analysis of power handling capability (PHC) between a reflective bandstop filter and an absorptive bandstop filter has been studied theoretically and experimentally in this dissertation.</div>

<div> <div> <div> <p>First-order methods such as Stochastic Gradient Descent are methods of choice for solving non-convex optimization problems in machine learning. These methods primarily rely on the gradient of the loss function to estimate descent direction. However, they have a number of drawbacks, including converging to saddle points (as opposed to minima), slow convergence, and sensitivity to parameter tuning. In contrast, second order methods that use curvature information in addition to the gradient, have been shown to achieve faster convergence rates, theoretically. When used in the context of machine learning applications, they offer faster (quadratic) convergence, stability to parameter tuning, and robustness to problem conditioning. In spite of these advantages, first order methods are commonly used because of their simplicity of implementation and low per-iteration cost. The need to generate and use curvature information in the form of a dense Hessian matrix makes each iteration of second order methods more expensive. </p><p><br></p> <p>In this work, we address three key problems associated with second order methods – (i) what is the best way to incorporate curvature information into the optimization procedure; (ii) how do we reduce the operation count of each iteration in a second order method, while maintaining its superior convergence property; and (iii) how do we leverage high-performance computing platforms to significant accelerate second order methods. To answer the first question, we propose and validate the use of Fisher information matrices in second order methods to significantly accelerate convergence. The second question is answered through the use of statistical sampling techniques that suitably sample matrices to reduce per-iteration cost without impacting convergence. The third question is addressed through the use of graphics processing units (GPUs) in distributed platforms to deliver state of the art solvers.</p></div></div></div><div><div><div> <p>Through our work, we show that our solvers are capable of significant improvement over state of the art optimization techniques for training machine learning models. We demonstrate improvements in terms of training time (over an order of magnitude in wall-clock time), generalization properties of learned models, and robustness to problem conditioning. </p> </div> </div> </div>

<div>Accurate prediction of electricity demand is a critical step in balancing the grid. Many factors influence electricity demand. Among these factors, climate variability has been the most pressing one in recent times, challenging the resilient operation of the grid, especially during climatic extremes. In this dissertation, fundamental challenges related to accurate characterization of the climate-energy nexus are presented in Chapters 2--4, as described below. </div><div><br></div><div>Chapter 2 explores the cost of neglecting the role of humidity in predicting summer-time residential electricity consumption. Analysis of electricity demand in the CONUS region demonstrates that even though surface temperature---the most widely used metric for characterising heat stress---is an important factor, it is not sufficient for accurately characterizing cooling demand. The chapter proceeds to show significant underestimations of the climate sensitivity of demand, both in the observational space as well as under climate change. Specifically, the analysis reveals underestimations as high as 10-15% across CONUS, especially in high energy consuming states such as California and Texas. </div><div><br></div><div>Chapter 3 takes a critical look at one of the most widely used metrics, namely, the Cooling Degree Days (CDD), often calculated with an arbitrary set point temperature of 65F or 18.3C, ignoring possible variations due to different patterns of electricity consumption across different regions and climate zones. In this chapter, updated values are derived based on historical electricity consumption data across the country at the state level. Chapter 3 analysis demonstrates significant variation, as high as +-25%, between derived set point variables and the conventional value of 65F. Moreover, the CDD calculation is extended to account for the role of humidity, in the light of lessons learnt in the previous chapter. Our results reveal that under climate change scenarios, the air-temperature based CDD underestimates thermal comfort by as much as ~22%.</div><div><br></div><div>The predictive analytics conducted in Chapter 2 and Chapter 3 revealed a significant challenge in characterizing the climate-demand nexuses: the ability to capture the variability at the upper tails. Chapter 4 explores this specific challenge, with the specific goal of developing an algorithm to increase prediction accuracy at the higher quantiles of the demand distributions. Specifically, Chapter 4 presents a data-centric approach at the utility level (as opposed to the state-level analyses in the previous chapters), focusing on high-energy consuming states of California and Texas. The developed algorithm shows a general improvement of 7% in the mean prediction accuracy and an improvement of 15% for the 90th quantile predictions.</div>

Quality drives customer satisfaction, improved business performance, and safer products. Reducing waste and variation is critical to the financial success of organizations. Today, it is common to see Lean and Six Sigma used as the two main strategies in improving Quality. As advancements in information technologies enable the use of big data, defect reduction and continuous improvement philosophies will benefit and even prosper. Predictive Quality Analytics (PQA) is a framework where risk assessment and Machine Learning technology can help detect anomalies in the entire ecosystem, and not just in the manufacturing facility. PQA serves as an early warning system that directs resources to where help and mitigation actions are most needed. In a world where limited resources are the norm, focused actions on the significant few defect drivers can be the difference between success and failure

Accuracy in turbulence modeling remains a hurdle in the widespread use of Computational Fluid Dynamics (CFD) as a tool for furthering fluids dynamics research. Meanwhile, computational power remains a significant concern for solving real-life wall-bounded flows, which portray a wide range of length and time scales. The tools for turbulence analysis at our disposal, in the decreasing order of their accuracy, include Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), and Reynolds-Averaged Navier Stokes (RANS) based models. While DNS and LES would remain exorbitantly expensive options for simulating high Reynolds number flows for the foreseeable future, RANS is and continues to be a viable option utilized in commercial and academic endeavors. In the first part of the present work, flow over the back-step test case was solved, and parametric studies for various parameters such as re-circulation length (X_r), coefficient of pressure (C_p), and coefficient of skin friction (C_f) are presented and validated with experimental results. The back-step setup was chosen as the test case as turbulent modeling of flow past backward-facing step has been pivotal to understand separated flows better. Turbulence modeling is done on the test case using RANS (k-ε and k-ω models), and LES modeling, for different values of Reynolds number (Re ∈ {2, 2.5, 3, 3.5} × 10⁴) and expansion ratios (ER ∈ {1.5, 2, 2.5, 3}). The LES results show good agreement with experimental results, and the discrepancy between the RANS results and experimental data was highlighted. The results obtained in the first part reveal a pattern of under-prediction noticed with using RANS-based models to analyze canonical setups such as the backward-facing step. The LES results show close proximity to experimental data, as mentioned above, which makes it an excellent source of training data for the machine learning analysis outlined in the second part. The highlighted discrepancy and the inability of the RANS model to accurately predict significant flow properties create the need for a better model. The purpose of the second part of the present study is to make systematic efforts to minimize the error between flow properties from RANS modeling and experimental data, as seen in the first part. A machine learning model was constructed in the second part of the present study to predict the eddy viscosity parameter (μt) as a function of turbulent kinetic energy (TKE) and dissipation rate (ε) derived from LES data, effectively working as an ad hoc eddy-viscosity based turbulence model. The machine learning model does not work well with the flow domain as a whole, but a zonal analysis reveals a better prediction of eddy viscosity than the whole domain. Among the zones, the area in the vicinity of the re-circulation zone gives the best result. The obtained results point towards the need for a zonal analysis for the better performance of the machine learning model, which will enable us to improve RANS predictions by developing a reduced order turbulence model.

The thesis presents the results of an empirical study of emotional intelligence on a sample of psychology students and managers, describes the features of socially-psychological training and seminars as active methods of formation of emotional intelligence. / В диссертации представлены результаты эмпирического исследования эмоционального интеллекта на выборке студентов-психологов и управленцев, описаны особенности социально-психологического тренинга и семинарских занятий как активных методов формирования эмоционального интеллекта.

<p>  This paper explored the application of three machine learning models focused on predictive motor maintenance. Logistic Regression, Sequential Minimal Optimization (SMO), and NaïveBayes models. A comparative analysis of these models illustrated that while each had an accuracy greater than 95% in this study, the Logistic Regression Model exhibited the most reliable operation.</p>

[ES] La presente tesis doctoral parte de la pregunta de investigación ¿es la Gamificación una metodología eficaz para el aprendizaje de historia de la educación española en la formación universitaria de futuros docentes? Cuatro ejes, explícitos en esta pregunta, son los que estructuran esta investigación educativa. El primero es la metodología de Gamificación, que fundamenta y conforma la investigación llevada a cabo. El segundo es la historia de la educación española, el contenido impartido a través de la experiencia gamificada. El tercero representa el contexto universitario en el que se desarrolla la investigación. Finalmente, el cuarto muestra la población con la que se hace el estudio de campo, alumnado de los grados de Magisterio de Infantil y Primaria. Este último eje tiene especial relevancia, ya que el desarrollo de investigación acción con futuros docentes se integra en la cartografía de la buena docencia universitaria (Paricio et al., 2019) y en la investigación desde el vínculo y la transferibilidad del conocimiento pedagógico durante el proceso. Los últimos estudios sobre Gamificación abogan por extraer resultados que evidencien el efecto de la Gamificación en variables más allá de la motivación. Además, enfatizan la necesidad de abordar la investigación desde un enfoque cualitativo y cuantitativo. La presente tesis atiende a estas premisas, por ello, con la finalidad de dar respuesta a la pregunta de investigación y comprobar las diferentes hipótesis, se acota un conjunto de variables cualitativas y cuantitativas. Las más significativas son metodología de Gamificación, competencias específicas, competencias transversales, motivación, rendimiento académico, rol docente y juego como recurso didáctico. Además, se utiliza un método mixto de investigación que combina un análisis cualitativo, ratificado con diferentes análisis cuantitativos para aportar validez ecológica a la investigación y mayor rigor a las conclusiones. Entre las conclusiones más relevantes destacan la satisfacción de los sujetos participantes hacia la experiencia gamificada, la mejora de las competencias específicas y el desarrollo de diferentes competencias transversales. Se logra también un incremento en la motivación y un buen rendimiento académico. Respecto al rol docente, la valoración es positiva, al igual que el juego como recurso didáctico. Los diferentes análisis también aportan conclusiones destacables relacionadas con el carácter ecléctico de la metodología, el éxito en la asistencia, la capacidad de atención, el grado de participación, la evaluación, la atención a la diversidad de alumnado o la percepción del propio proceso de aprendizaje. Además, esta investigación acción considera los últimos estudios en Gamificación e investigación educativa y responde a las demandas y retos que estos plantean. De este modo, trata de lograr la proyección internacional a la que se aspira con esta tesis doctoral y colabora en la creación de conocimiento empírico en el campo de la Gamificación. / [CA] La present tesi doctoral part de la pregunta d'investigació ¿és la Gamificació una metodologia eficaç per a l'aprenentatge d'història de l'educació espanyola en la formació universitària de futurs docents? Quatre eixos, explícits en aquesta pregunta, són els que estructuren aquesta investigació educativa. El primer és la metodologia de Gamificació, que fonamenta i conforma la investigació duta a terme. El segon és la història de l'educació espanyola, el contingut impartit a través de l'experiència gamificada. El tercer representa el context universitari en el qual es desenvolupa la investigació. Finalment, el quart mostra la població amb la qual es fa l'estudi de camp, alumnat dels graus de Magisteri d'Infantil i Primària. Aquest últim eix té especial rellevància, ja que el desenvolupament d'investigació acció amb futurs docents s'integra en la cartografia de la bona docència universitària (Paricio et al., 2019) i en la investigació des del vincle i la transferibilitat del coneixement pedagògic durant el procés. Els últims estudis sobre Gamificació advoquen per extraure resultats que evidencien l'efecte de la Gamificació en variables més enllà de la motivació. A més, emfatitzen la necessitat d'abordar la investigació des d'un enfocament qualitatiu i quantitatiu. La present tesi atén aquestes premisses, per això, amb la finalitat de donar resposta a la pregunta d'investigació i comprovar les diferents hipòtesis, es delimita un conjunt de variables qualitatives i quantitatives. Les més significatives són metodologia de Gamificació, competències específiques, competències transversals, motivació, rendiment acadèmic, rol docent i joc com a recurs didàctic. A més, s'utilitza un mètode mixt d'investigació que combina una anàlisi qualitativa, ratificat amb diferents anàlisis quantitatives per a aportar validesa ecològica a la investigació i major rigor a les conclusions. Entre les conclusions més rellevants destaquen la satisfacció dels subjectes participants cap a l'experiència gamificada, la millora de les competències específiques i el desenvolupament de diferents competències transversals. S'aconsegueix també un increment en la motivació i un bon rendiment acadèmic. Respecte al rol docent, la valoració és positiva, igual que el joc com a recurs didàctic. Les diferents anàlisis també aporten conclusions destacables relacionades amb el caràcter eclèctic de la metodologia, l'èxit en l'assistència, la capacitat d'atenció, el grau de participació, l'avaluació, l'atenció a la diversitat d'alumnat o la percepció del propi procés d'aprenentatge. A més, aquesta investigació acció considera els últims estudis en Gamificació i investigació educativa i respon a les demandes i reptes que aquests plantegen. D'aquesta manera, tracta d'aconseguir la projecció internacional a la qual s'aspira amb aquesta tesi doctoral i col·labora en la creació de coneixement empíric en el camp de la Gamificació. / [EN] The forthcoming doctoral thesis stems from the research question, is gamification an efficient method for learning the history of Spanish education in university training for future teachers? Four themes, which are explicit in the question itself, will give structure to this educational research. The first is the methodology of Gamification, which constitutes the grounds on which this investigation is based. The second is the history of Spanish education, the content delivered via a gamified experience. The third represents the university context in which the research takes place. Lastly, the fourth shows the demographic of those involved in the field study, alumni from Primary and Nursery School Teaching university degrees. This last theme is particularly relevant, as developing action research with future teachers is an integral part of mapping out quality university teaching (Paricio et al., 2019) and of binding research and transferability of pedagogic knowledge during the process. The most recent studies on Gamification advocate for extracting results that underline the effects of Gamification in variables beyond motivation. Furthermore, they emphasize the need to approach the research from a qualitative and quantitative point of view. Thereupon, the present doctoral thesis handles these propositions with the aim to answer the research question and prove different hypotheses, narrowing down a group of qualitative and quantitative variables. The most significant are Gamification methodology, specific competences, transversal competences, motivation, academic performance, the role of the lecturer and games as a teaching resource. In addition, a mixed research method is used which combines qualitative analysis, reinforced by different quantitative analyses in order to provide ecological validity to the study and more rigour to the conclusions. Among the most relevant conclusions, the following stand out: the satisfaction of the participating subjects towards the gamified experience, progress in specific competences, and the development of different transversal competences. Regarding the lecturer's role, the assessment is positive, as well as games as a teaching resource. The different analyses also provide noteworthy conclusions related to the eclectic nature of the methodology, attendance success, attention capacity, degree of participation, assessment, attention to student diversity or the perception of the process of learning itself. In addition, this action research examines the latest Gamification and educational research and answers the demands and challenges that they posit. In so doing, it attempts to achieve the international outreach to which this doctoral thesis aspires, and it assists in the creation of empiric knowledge in the field of Gamification. / Edo Agustín, E. (2021). La metodología de Gamificación para el aprendizaje de historia de la educación española: investigación acción en la formación universitaria de docentes [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/178971

Text in English / In Swaziland, family nurse practitioners (FNPs) are professional nurses who have undergone preparation as general nurse, midwife and FNP. These nurses play an important role in the delivery of primary health care (PHC). Family nurse practice is an evolving concept introduced in Swaziland in 1979. It is a means of exploring nursing roles and primary health care services for deployment in under-served areas and to enable nurses to serve as the primary providers of health care services in clinics, health centres and in the outpatient departments of hospitals. Changing responsibilities within the health care setting require different skills and more knowledge. The expansion and extension of the nurses' role, including the techniques of diagnosing and treating, was a priority of the Ministry of Health and Social Welfare (MOH&SW) in Swaziland's five-year development plan for 1978-1983. It was regarded as a necessary component for raising the quality and effectiveness of PHC services. Some of the major and urgent challenges that confront FNPs today are the advent of the human immune virus/acquired immuno-deficiency syndrome (HIV/AIDS) scourge and the re-emergence of the tuberculosis epidemic. Both these health problems require proficient diagnosis and case management skills as well as new approaches. If FNPs are to remain relevant and to continue to provide quality services in spite of prevailing challenges, they have to engage in continuing education (CE). The main aim of this study was to investigate the perceptions of the FNP role, CE needs and issues relevant to the current practice of FNPs in Swaziland. A further aim was to establish a structure or framework for a CE programme that would contribute to the strengthening of CE for FNPs and identify enabling factors and barriers in the practice and education ofFNPs. Both quantitative and qualitative research methods were used for data collection. A survey was conducted to collect data from 5 7 FNPs and 11 nurse managers and nurse educators. The transcript from the questionnaires was subjected to quantitative-based content analysis. A total of thirty nurse managers, nurse educators and MOH&SW nurse executives participated in the focus group interviews. The collected data was subjected to qualitativebased content analysis. The findings identified the role of the FNP as manager, clinical practitioner, educator and researcher. The analyses highlighted the CE needs of FNPs, and the question of updating and upgrading the skills of practising FNPs. The identified enabling factors and barriers, although perceived as issues that are peripheral and auxiliary to the curriculum, appeared to have a strong bearing on programme planning. The findings from this study have implications for a structured CE programme for FNPs at the University of Swaziland. / Health Studies / D. Litt et Phil. (Nursing Sciences)

Text in English / In Swaziland, family nurse practitioners (FNPs) are professional nurses who have undergone preparation as general nurse, midwife and FNP. These nurses play an important role in the delivery of primary health care (PHC). Family nurse practice is an evolving concept introduced in Swaziland in 1979. It is a means of exploring nursing roles and primary health care services for deployment in under-served areas and to enable nurses to serve as the primary providers of health care services in clinics, health centres and in the outpatient departments of hospitals. Changing responsibilities within the health care setting require different skills and more knowledge. The expansion and extension of the nurses' role, including the techniques of diagnosing and treating, was a priority of the Ministry of Health and Social Welfare (MOH&SW) in Swaziland's five-year development plan for 1978-1983. It was regarded as a necessary component for raising the quality and effectiveness of PHC services. Some of the major and urgent challenges that confront FNPs today are the advent of the human immune virus/acquired immuno-deficiency syndrome (HIV/AIDS) scourge and the re-emergence of the tuberculosis epidemic. Both these health problems require proficient diagnosis and case management skills as well as new approaches. If FNPs are to remain relevant and to continue to provide quality services in spite of prevailing challenges, they have to engage in continuing education (CE). The main aim of this study was to investigate the perceptions of the FNP role, CE needs and issues relevant to the current practice of FNPs in Swaziland. A further aim was to establish a structure or framework for a CE programme that would contribute to the strengthening of CE for FNPs and identify enabling factors and barriers in the practice and education ofFNPs. Both quantitative and qualitative research methods were used for data collection. A survey was conducted to collect data from 5 7 FNPs and 11 nurse managers and nurse educators. The transcript from the questionnaires was subjected to quantitative-based content analysis. A total of thirty nurse managers, nurse educators and MOH&SW nurse executives participated in the focus group interviews. The collected data was subjected to qualitativebased content analysis. The findings identified the role of the FNP as manager, clinical practitioner, educator and researcher. The analyses highlighted the CE needs of FNPs, and the question of updating and upgrading the skills of practising FNPs. The identified enabling factors and barriers, although perceived as issues that are peripheral and auxiliary to the curriculum, appeared to have a strong bearing on programme planning. The findings from this study have implications for a structured CE programme for FNPs at the University of Swaziland. / Health Studies / D. Litt et Phil. (Nursing Sciences)