No description available.

No description available.

No description available.

The field of infinite series is so large that any investigation into that field must necessarily be limited to a particular phase. An attempt has been made to develop a number of tests having a wide range of applications. Particular emphasis has been placed on tests for series of positive terms.

This thesis explores methods for analyzing biomedical time-series data, focusing on two distinct applications: audio-based cough detection and time-gate optimization in Fluorescence Lifetime Imaging Microscopy (FLIM). The first section presents time-gate optimization algorithm for rapid lifetime determination (RLD) in FLIM applications. FLIM is an emerging imaging technique used to measure molecular interactions in biological samples. The developed algorithm focuses on optimizing the time gates to balance speed and accuracy, which is particularly beneficial under diverse noise conditions. By maximizing the signal-to noise ratio (SNR), the algorithm improves the precision of lifetime measurements, enabling efficient analysis of biological processes that require fast imaging rates, such as cellular metabolism and neurological activities. The second section presents a machine learning algorithm for automated cough detection using Convolutional Recurrent Neural Networks (CRNNs). Leveraging advanced feature extraction techniques, such as Mel spectrograms, the algorithm effectively distinguishes cough events from other audio signals, achieving high accuracy. Its adaptability to varying noise conditions makes it ideal for real-time respiratory monitoring, with strong potential for integration into mobile health platforms and hospital systems. This work addresses the critical need for non-invasive, continuous monitoring tools for chronic cough, a condition that significantly affects quality of life. Both contributions highlight the potential of targeted time-series analysis to improve the accuracy, speed, and reliability of biomedical monitoring and imaging. By advancing methods for cough detection and fluorescence lifetime estimation, this thesis offers adaptable tools for broader biomedical applications, contributing to both healthcare diagnostics and biological research. / Thesis / Master of Applied Science (MASc)

The purpose of this paper is to examine certain questions concerning infinite series. The first chapter introduces several basic definitions and theorems from calculus. In particular, this chapter contains the proofs for various convergence tests for series of real numbers. The second chapter deals primarily with the equivalence of absolute convergence, unconditional convergence, bounded multiplier convergence, and c0 multiplier convergence for series of real numbers. Also included in this chapter is a proof that an unconditionally convergent series may be rearranged so that it converges to any real number desired. The third chapter contains a proof of the Silverman-Toeplitz Theorem together with several applications.

Through a close analysis of Suzanne Collins’ Hunger Games series and Veronica Roth’s Divergent series, it will be shown that these two-current young adult dystopian book-film crossovers pose several relevant parallels to contemporary real-world problems. By deciphering a pattern on what garners their popularity, and most importantly analyzing the aspect of why they reached such levels of recognition, we can then begin to close in on just how important these two series are in representing the 21st century young American mindset. Taking into the equation also, how the overall-arching genre of dystopia has evolved with the times and has now adapted to reflect contemporary anxieties and fears. Looking into several elements such as a newfound desire for strong female roles, persuasive antagonists that are inspired by realistic historical precedents, and an unsettling desensitization towards violence and gore, we can then see that the successful equation of The Hunger Games and Divergent series reflects mainstream interests evocatively and effectively. It is not just an intervention into the encompassing utopian/dystopian tradition, but into today’s sociology. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

En el cuerpo de los números reales un resultado clásico de Riemann (1854) afirma que si tenemos una serie condicionalmente convergente entonces al cambiar el orden de los sumandos es posible hacerla converger a cualquier número deseado, o hacerla diverger. En el caso de series de números complejos condicionalmente convergentes podemos reordenar las partes reales (o imaginarias) y obtener cualquier suma prefijada; pero esta misma reordenación también afecta a la parte imaginaria (o real), pudiendo esta diverger, por tanto hacer que toda la serie de términos complejos diverja y no habremos conseguido nada. Entonces podemos preguntarnos: ¿Cuál es el correspondiente teorema para series de números complejos? P. Lévy (1905) probó que “el conjunto de todas las reordenaciones de una serie de números complejos es el vacío o la traslación de un subespacio vectorial real”. Este resultado fue generalizado a un espacio vectorial real n-dimensional por E. Steinitz (1913) que es uno de los capítulos que pretendemos estudiar en este trabajo de tesis de una manera accesible e interesante. De la misma manera nos podemos preguntar: ¿Cuál es la situación para espacios de Banach infinito dimensionales, se cumplirá el resultado de Steinitz? La respuesta a esta pregunta es negativa gracias a un contraejemplo propuesto por Marcinkiewicz en el espacio L2r0, 1s. Ahora lo natural es estudiar a que tipos de espacios se puede extender el resultado de Steinitz, es decir, dar condiciones a ciertos espacios de dimensión infinita para que el teorema de Steinitz se mantenga. Por ejemplo, W. Banaszczyk en [13] y [14], prob´o que un espacio de Fr´echet es Nuclear si y sólo si se cumple el teorema de Lévy-Steinitz. / Tesis

La telenovela es el producto de mayor consumo en Latinoamérica. En nuestro país, representa el 13,5%1 de la oferta televisiva. La hibridación de los géneros y formatos hacen que la telenovela tenga nuevos rasgos particulares. “Al fondo hay sitio” (AFHS) es producto de esta hibridación, desde su estructura narrativa hasta el contenido de la misma. En este sentido, el número de episodios no responde al formato clásico de telenovela latina, sino, a la combinación de telenovela con sitcom y soap opera, en tanto que el número de episodios sobrepasa el promedio de 120 capítulos (que es el estándar en Latinoamérica), pues, hasta la fecha, ya han sobrepasado los 700 capítulos emitidos2 . Hay que añadir que AFHS responde más a la estructura del sitcom o del soap opera dado que el relato se prolonga en temporadas. Asimismo, encontramos que hay personajes cuyo perfil dramático “no evoluciona” ante giros dramáticos que deberían cambiar su forma de ser actuar, etc. Ello no sucede y el personaje se queda en el mismo statu quo. También, AFHS apela a las identidades culturales del público, a través de la caracterización de sus personajes o de los hechos narrados. Este fenómeno que se está creando en nuestro país es lo que se desea investigar, desde la perspectiva de las manifestaciones culturales (la identidad cultural) representadas en los personajes y las situaciones dramáticas del relato (AFHS). / Tesis

[pt] Este trabalho apresenta um modelo linear por partes chamado de modelo ARN. Trata-se de uma estrutura híbrida que envolve modelos autoregressivos e redes neurais. Este modelo é comparado com o modelo AR de coeficientes fixos e com a rede neural estática aplicada à previsão. Os resultados mostram que o ARN consegue identificar a estrutura não-linear dos dados simulados e que na maioria dos casos ele possui melhor habilidade preditiva do que os modelos supracitados. / [en] In this thesis we develop a piece-wise linear model named ARN model. Our model has a hybrid structure which combines autoregressive models and neural networks. We compare our model to the fixed-coefficient AR model and to the prediction static neural network. Our results show that ARN is able to find the non-linear structure of simulated data and in most cases it performs better than the methods mentioned above.