An important goal of Educational Data Mining is to provide data and visualization about students’ state of knowledge and students’ affective states. The combination of these provides an understanding of the easiness or hardness of the concepts being taught and the student’s comfortability in it. While various studies have been conducted on estimating students’ knowledge and affect, little research has been done to transform this collected (Raw) data into meaningful information that is more relatable to teachers, parents and other stakeholders, i.e. Non-Researchers. This research seeks to enhance existing Teacher Tools (An application designed within the MathSpring - An Intelligent Tutoring system) to generate a live dashboard for teachers to use in the classroom, as their students are using MathSpring. The system captures student performance and detects students’ facial expressions, which highlight students emotion and engagement, using a deep learning model that detects facial expressions. The live dashboard enables teachers to understand and juxtapose the state of knowledge and corresponding affect of students as they practice math problem solving. This should help teachers understand students’ state of mind better, and feed this information back to act and alter their instruction or interaction with each student in a personalized way. We present results of teachers' perceptions of the usefulness of the Live Dashboard, through a qualitative and quantitative survey.

Diploma thesis deals with design of a model for currency rate prediction by using artificial intelligence as a tool for decision making process in business and public administration. Concrete usage of this prediction is applied in company TechPlasty s.r.o. The thesis focuses on analysis of input data, optimization of a prediction model and evaluation of the results and their profit for the selected company.

The feedback control system is defined as the sampling of an output signal and feeding it back to the input, resulting in an error signal that drives the overall system. This dissertation focuses on the stabilization and performance of state feedback control systems. Chapters 3 and 4 focus on the feedback control protocol approaching in the multi-agents system. In particular, the global regulation of distributed optimization problems has been considered. Firstly, we propose a distributed optimization algorithm based on the proportional-integral control strategy and the exponential convergence rate has been delivered. Moreover, a decentralized mechanism has been equipped to the proposed optimization algorithm, which enables an arbitrarily chosen agent in the system can compute the value of the optimal solution by only using the successive local states. After this, we consider the cost function follows the restricted secant inequality. A dynamic event-triggered mechanism design has been proposed. By ensuring the global regulation of the distributed proportional-integral optimization algorithm, the dynamic event-triggered mechanism efficiently reduces the communication frequency among agents. Chapter 5 focuses on the feedback control protocol approaching the single-agent system. Specifically, we investigate the truncated predictor feedback control of the regulation of linear input-delayed systems. For the purpose of improving the closed-loop performance, we propose a design of the truncated predictor feedback method with time-varying feedback parameters and give the potential range of choosing the time-varying feedback parameters to replace the traditional constant low gain parameters.

Health care professionals and nurse educators are concerned about increasing percentage of first time test takers failing the NCLEX-RN exam. The purpose of this retrospective study was to examine predictive accuracy of the Assessment Technologies Institute (ATI) RN Comprehensive Predictor for passing or failing the NCLEX-RN exam in a cohort of nursing students (N = 195). South Eastern Technical College in South Carolina is in jeopardy of losing accreditation for their nursing program because of the low percentage of first time test takers failing. The college's pass rate on the NCLEX-RN exam has been below the national and state averages for the last 2 years. Guided by Bloom's taxonomy and Knowles' andragogical model of learning as the theoretical mainstay, factors that predict success with the NCLEX-RN exam for first time test takers were examined. Inferential and descriptive statistics were used to determine if a relationship existed between NCLEX-RN scores and the independent variables. A Pearson r correlation test was conducted to address whether the ATI Comprehensive Predictor accurately predict student success with the NCLEX-RN exam on first attempt. Multiple regression was employed to test for a significant relationship between prenursing GPA, final GPA, age, gender, and ATI predictor scores. Regression analysis results showed ATI Predictor scores to significantly predict student success with the NCLEX-RN exam on first attempt. Based on findings, a Structured Learning Assistance program was proposed to assist in preparing students for NCLEX-RN success. Positive social change occurs within the community, nursing programs, and health care by increased NCLEX-RN pass rates enhancing the number of nurses entering into health care.

No description available.

Vegetation has been identified to play a significant role in river environments by providing a wide range of ecosystem services. For this reason, the use of plants has become relevant in river restoration projects. However, the presence of plants in channel beds increases the flow resistance and, thus, the water levels during flood conditions. Additionally, river vegetation, whether instream or riparian, influences the morphological evolution of rivers. Observations show that instream vegetation has a strong impact on bedload transport. Yet, there is a scarcity of sediment transport predictors that directly account for the effects of plants, and existing methods, based on re-calculation of roughness coefficients, may present some inconsistencies. Therefore, an approach that extends Einstein’s (1950) parameters to include the effects of vegetation geometry and spatial density on sediment transport is herein proposed. The new formulations of the dimensionless transport parameter Φ and the flow intensity parameter Ψ were derived for their implementation in existing bedload predictors of the form Φ = (Ψ). The applicability of this new approach considers the presence of submerged and emergent vegetation, but reduces to the original Einstein’s model if vegetation is absent. The research methodology was carried out in four phases. First, a comprehensive literature review for the identification of, mainly, the different effects of vegetation on river morphodynamics, the state-of-the-art knowledge on the flow-sediment-vegetation interactions, and the current approaches to bedload estimation in channels with vegetated beds. Second, the derivation of the extended Einstein’s parameters, starting from a momentum balance for a control volume of a generic channel with instream submerged vegetation (as proposed by Petryk and Bosmajian, 1975). Third, an extensive experimental program carried out on a tilting flume with a mobile bed and with plants being represented by series of aluminum cylinders. Different scenarios of vegetation spatial density were tested while measurements of bedload rate, water level, bed level and flow velocity were periodically performed in order to assess conditions of stationarity and morphodynamic equilibrium. Last, a deep analysis of experimental results allowed for the calibration of the new approach, whereas external datasets from the literature were used to assess its performance in a wide variety of conditions. A study based on four statistical measures showed that the extended Einstein’s parameters are significantly more suitable for bedload rate estimation when compared to the original ones, since predicted and measured values have, on average, the same order of magnitude. Additionally, the new approach outperformed the widely-adopted method of Baptist (2005), which consists of the re-calculation of bed roughness in vegetated settings. Finally, the experimental observations suggest that the submergence ratio and the stem spatial density are the most important traits of river plants to display influence on bedload transport, channel bed stability, and bed form dimensions and patterns. A better understanding of these traits might lead to better prediction capabilities of river evolution. / La vegetazione svolge un ruolo fondamentale negli ambienti fluviali, poiché fornisce un ampio spettro di servizi ecosistemici; per questo essa è una componente rilevante dei progetti di riqualificazione fluviale. Tuttavia, la presenza di piante in alveo aumenta la resistenza al moto e di conseguenza anche il tirante idrico durante gli eventi di piena. Inoltre, la copertura vegetale in alveo e nelle zone riparie influenza l'evoluzione morfologica dei corsi d'acqua. Nonostante le evidenze sperimentali mostrino che la vegetazione in alveo ha un forte impatto sul trasporto dei sedimenti, sono poche le formule di trasporto che tengono conto in modo esplicito dell'effetto della vegetazione e i metodi esistenti, basati sulla determinazione di un coefficiente di scabrezza, possono dare luogo a incongruenze. Per questa ragione, in questa tesi si propone un approccio che estende la formulazione di Einstein (1950) e include l'effetto della geometria e della densità spaziale della vegetazione sul trasporto solido. Sono state derivate nuove espressioni per il parametro di trasporto adimensionale Φ e il parametro di intensità del trasporto Ψ, che possono essere introdotte in modelli di trasporto esistenti del tipo Φ = f(Ψ). Questo nuovo approccio consente di considerare l'effetto della presenza di vegetazione sommersa ed emergente e si riduce al modello originale di Einstein in assenza di vegetazione. L'attività di ricerca si è svolta in quattro fasi. Nella prima fase si è svolta un'analisi approfondita della letteratura mirata soprattutto a identificare gli effetti della vegetazione sulla morfodinamica fluviale, definire lo stato dell'arte relativo alle interazioni fra flusso liquido, sedimenti e vegetazione, ed analizzare gli approcci esistenti per la stima del trasporto di fondo in alvei vegetati. Nella seconda fase si sono derivati i parametri della formulazione di Einstein estesa a partire dal bilancio di quantità di moto per un volume di controllo di un canale generico con vegetazione sommersa (come proposto da Petryk e Bosmajian, 1975). Nella terza fase è stato condotto un esteso set di esperimenti, utilizzando un modello fisico costituito da una canaletta di laboratorio a pendenza variabile e fondo mobile, in cui le piante sono state simulate tramite cilindri in alluminio. Sono stati riprodotti diversi scenari di densità spaziale della vegetazione e sono stati misurati periodicamente la portata solida, la quota della superficie libera e del fondo e la velocità della corrente per valutare le condizioni di stazionarietà ed equilibrio morfodinamico. Infine, il nuovo approccio è stato calibrato sulla base di un'analisi approfondita dei risultati sperimentali e quindi applicato a set di dati di letteratura per valutarne l'accuratezza in un ampio intervallo di condizioni. Un'analisi statistica basata su quattro indicatori ha mostrato che i parametri della formulazione di Einstein estesa producono stime di trasporto solido sensibilmente più accurate rispetto ai parametri originali, in quanto i valori calcolati sono, in generale, dello stesso ordine di grandezza dei valori misurati. Inoltre, il nuovo approccio dà risultati migliori rispetto al metodo di Baptist (2005), ampiamente adottato, che consiste nel ricalcolo della scabrezza per gli alvei vegetati. Infine, le osservazioni sperimentali suggeriscono che il rapporto di sommergenza e la densità spaziale delle piante sono i parametri che influenzano in modo più significativo il trasporto solido, la stabilità del fondo dell'alveo, la scala delle forme di fondo e la loro organizzazione spaziale. Una conoscenza più approfondita di questi aspetti può contribuire a una maggiore capacità di prevedere l'evoluzione dei corsi d'acqua. / Se ha identificado a la vegetación como un actor importante en ambientes fluviales al proporcionar una amplia gama de servicios ecosistémicos. Por esta razón, el uso de plantas se ha vuelto cada vez más relevante en proyectos de restauración de ríos. Sin embargo, su presencia en lechos fluviales impacta la resistencia al flujo, aumentando los niveles del agua en condiciones de inundación. Además, este tipo de vegetación, ya sea que esté en el lecho o en las márgenes, influye en la evolución morfológica de los ríos. Diversas observaciones han mostrado que la vegetación fluvial tiene un fuerte impacto en las tasas de transporte sólido de fondo. A pesar de ello, hay una escasez de métodos confiables para la estimación de este tipo de sedimentos que tome en consideración el efecto de las plantas y, aquéllos que existen, los cuales se basan en la corrección del coeficiente de rugosidad del canal, suelen presentar resultados inconsistentes. Por tanto, se propone aquí un método que extiende las definiciones fundamentales de Einstein (1950) en modo que se incluyan los efectos de la geometría y la densidad espacial de las plantas sobre el transporte sólido. Las nuevas ecuaciones del parámtero de transporte, Φ, y el parámetro de movilidad, Ψ, fueron obtenidas para su implementación en métodos predictores de transporte de fondo de la forma Φ = (Ψ). La aplicabilidad de este nuevo enfoque considera la posibilidad de vegtación fluvial tanto emergente como sumergida, y se reduce a las ecuaciones originales de Einstein si ésta fuera inexistente. La metodología de investigación se llevó a cabo en cuatro fases. Primero, una revisión exhaustiva de la literatura para la identificación, principalmente, de los diferentes efectos de la vegetación en la morfodinámica de ríos, los avances más recientes en el conocimiento sobre las interacciones flujo-sedimento-vegetación, y los métodos actualmente existentes para la estimación del transporte sólido de fondo en canales naturales vegetados. En segundo lugar, la obtención de los parámetros de Einstein extendidos a partir de un balance de momentum para el volumen de control de un canal genérico con vegetación sumergida (según lo propuesto por Petryk y Bosmajian, 1975). En tercer lugar, un extenso programa experimental realizado en un canal de fondo móvil y pendiente variable, con las plantas siendo representadas por series de cilindros metálicos. Se probaron diferentes escenarios de densidad espacial de vegetación, mientras que periódicamente se realizaron mediciones transporte sólido, niveles del agua, topografía del fondo y velocidad del flujo con el objeto de evaluar las condiciones de flujo uniforme y equilibrio morfodinámico. Por último, un análisis profundo de los resultados experimentales permitió la calibración del nuevo método, mientras que se utilizaron datos externos disponibles en la literatura para evaluar su desempeño bajo diversas condiciones. Un estudio basado en cuatro medidas estadísticas mostró que los parámetros extendidos de Einstein son mucho más adecuados para la estimación del transporte de fondo en comparación con los originales, ya que los valores estimados y los medidos muestran, en promedio, el mismo orden de magnitud. Además, el nuevo método superó al propuesto por Baptist (2005), ampliamente utilizado, el cual consiste en la corrección de la rugosidad del canal en presencia de vegetación. Finalmente, las observaciones experimentales sugieren que la sumergencia de las plantas y la densidad espacial de los tallos son las variables más influyentes en el transporte sedimentos de fondo, la estabilidad del lecho, y las dimensiones y patrones de la forma de fondo. Una mejor comprensión de estas variables puede significar una mejor capacidad para predecir la evolución de un río.

No description available.

A pitch-control predictor-corrector ascent guidance algorithm has been developed and evaluated for a rocket-based upper stage of a two-stage-to-orbit launch vehicle. Detailed descriptions of the predictor-corrector algorithm and a neural network loop modification are given. The mission requirement is insertion into a stable 50 x 100 nmi orbit at 375,000 ft altitude, coasting toward apogee at a positive inertial flight path angle. Three degree-of-freedom trajectory analysis is performed using the Program to Optimize Simulated Trajectories (POST2). Results of Monte Carlo simulations including uncertainties in atmosphere, thrust, aerodynamics and initial state are presented and compared to trajectories optimized for maximum injected weight. / Master of Science

Blind equalization schemes are used to cancel the effects of a channel on the received signal when the transmission of a training sequence in a predefined time slot is not possible. In the absence of a training sequence, blind equalization schemes can also increase the throughput of the overall system. A general problem with blind adaptation techniques is that they have poor convergence properties compared to the traditional techniques using training sequences. Having a multi-modal cost surface, blind adaptation techniques may force the equalizer to converge to a false minimum, depending on the initialization. The most commonly used blind adaptation algorithm is the Constant Modulus Algorithm (CMA). It is shown by simulation that a logarithmic error equation can make CMA converge to a global minimum, if a differential encoding scheme is used. The performance of CMA with different error equations is also investigated for different channel conditions. For a time varying channel, the performance of an equalizer not only depends on the convergence behavior but also on the tracking property, which indicates the ability of an equalizer to track changes in the channel. The tracking property of a blind equalizer with CMA has been investigated under different channel conditions. It is also shown that the tracking property of a blind equalizer can be improved by using a recursive linear predictor at the output of the equalizer to predict the amplitude of the equalizer output. The predicted value of the amplitude is then used to adjust the instantaneous gain of the overall system. A recursive linear predictor is designed to predict a colored signal without having a priori knowledge about the correlation function of the input sequence. The performance of the designed predictor is also investigated by predicting the envelope of a flat fading channel under constant mobile velocity and constant acceleration conditions. / Master of Science

Background: Total knee arthroplasty (TKA) is the treatment of choice for end-stage knee osteoarthritis. Growth in the number of procedures performed annually in the United States is expected to increase steadily. Post-operative rehabilitation settings vary and include both institutional and community based physical therapy (PT) services. Despite access to PT, deficits in gait often persist for months and even years after surgery. Slow gait speed, asymmetrical walking patterns, and prolonged time in double-limb support following the TKA often lead to the need for an assistive device for walking and prolong the rehabilitation phase. Purpose: The purpose of this study is to analyze early gait during inpatient rehabilitation to quantify both the improvements made and deficits that remain in important gait variables. This study identifies predictor variables that contribute to the variance in discharge ambulation device use and IRF length of stay. Subjects: A convenience sample of 230 patients discharged to an IRF following a TKA (160 following a single TKA and 70 following a bilateral procedure) was used for this analysis. Method: Paired t-tests were used to compare temporal and spatial gait variables from the initial gait assessment compared to the discharge gait assessment in patients following single TKA to determine remaining deficits. Right vs left comparisons were made for patients following a bilateral procedure. A binary logistic regression was used to identify predictors associated with the need for a two-handed ambulation device at discharge. A multiple linear regression developed a model to assess predictors of the inpatient rehabilitation length of stay. Finally, a self-assessment to evaluate patient confidence with walking (mGES scale) was correlated to actual gait speed performed on the gait analysis in a sample of patients from our study population. Findings: Deficits in step length, step time and percent of single limb support remained in the involved limb compared to uninvolved limb at discharge from inpatient rehabilitation following single TKA; no limb differences between the right and left side were noted in patients after bilateral TKA. The discharge gait speed of 54.6 cm/sec for single TKA patients and discharge speed of 61.5 cm/sec for bilateral TKA patients is within the classification of limited community ambulators and making them appropriate for a home discharge. But despite improvement from admission to discharge, the gait speed for both groups in our study remain below the gait speed identified by prior studies 3-months following TKA surgery where speed reached 135 cm/sec. The need for a two-handed ambulation device, such as bilateral canes or a walker, was associated with slow walking speed and prior use of a device before surgery. A longer rehabilitation length of stay was associated with slower initial gait speed, lower motor FIM scores and reduced knee extension at admission. The mGES patient self-report conducted at the time of the discharge gait assessment showed a moderate correlation to the discharge gait speed; however, the pairing of the admission mGES with the admission gait speed was not significantly correlated.