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243 p., Los G-quadruplex han suscitado un gran interés en los últimos años para desarrollar terapias contra el cáncer. Estas estructuras de ADN pueden encontrarse en los telómeros y/o promotores de oncogenes, y se ha observado que la estabilización de tales G-quadruplexos puede alterar el crecimiento de células tumorales. Sin embargo, los mecanismos que conducen a la formación y estabilización de estos G-quadruplexos aún no están bien establecidos y son el foco de muchos trabajos actuales en este campo. En esta tesis se ha estudiado la interacción de dos isómeros, ecuatorial y axial, del complejo metálico [Mo(¿3-C3H5)Br(CO)2(phen)] con diferentes estructuras de ADN, prestando especial interés en el ADN G-quadruplexo, mediante métodos computacionales para conocer su citotoxicidad ya encontrada previamente mediante métodos experimentales. Entre todos los métodos utilizados, la Teoría del Funcional de la Densidad de Escalamiento Lineal (LS-DFT) tiene una especial relevancia a lo largo de este trabajo, pero también se han realizado cálculos con métodos como el semi-empírico, el DLPNO-CCSD(T), y el QM/MM, complementados con EDA, así como el uso de herramientas computacionalespara el análisis de enlaces débiles, QTAIM y NCI. Las energías de formación calculadas, el análisis de descomposición de energía, las energías de solvatación y el análisis de interacciones no covalentes explican la afinidad y la selectividad del complejo metálico por el ADN G-quadruplexo sobre el ADN de doble hélice. También se ha observado que, entre los dos isómeros complejos metálicos estudiados, el complejo Axial es favorable para la interacción con el ADN G-quadruplexo debido a la disposición que adopta el complejo, completamente insertado en la cavidad de la estructura del G-quadruplexo, dispuesto entre tétradas, favoreciendo la creación de interacciones débiles estabilizadoras. Las interacciones débiles más relevantes son las relacionadas con el apilamiento ¿- ¿ debido a que la superficie aromática pl

Role of dispersion interactions in Endohedral TM@(ZnS)(12) structures, [EN] II−VI semiconducting materials are gaining attention due to their optoelectronic properties. Moreover, the addition of transition metals, TMs, might give them magnetic properties. The location and distance of the TM are crucial in determining such magnetic properties. In this work, we focus on small hollow (ZnS)12 nanoclusters doped with TMs. Because (ZnS)12 is a cage-like spheroid, the cavity inside the structure allows for the design of endohedral compounds resembling those of C60. Previous studies theoretically predicted that the first-row TM(ZnS)12 endohedral compounds were thermodynamically unstable compared to the surface compounds, where the TM atom is located at the surface of the cluster. The transition states connecting both structure families were calculated, and the estimated lifetimes of these compounds were predicted to be markedly small. However, in such works dispersion effects were not taken into account. Here, in order to check for the influence of dispersion on the possible stabilization of the desired TM(ZnS)12 endohedrally doped clusters, several functionals are tested and compare to MP2. It is found that the dispersion effects play a very important role in determining the location of the metals, especially in those TMs with the 4s3d shell half-filled or completely filled. In addition, a complete family of TM doped (ZnS)12 nanoclusters is explored using ab initio molecular dynamics simulations and local minima optimizations that could guide the experimental synthesis of such compounds. From the magnetic point of view, the Cr(7S)@(ZnS)12 compound is the most interesting case, since the endohedral isomer is predicted to be the global minimum. Moreover, molecular dynamics simulations show that when the Cr atom is located at the surface of the cluster, it spontaneously migrates toward the center of the cavity at room temperature.

This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.

Role of dispersion interactions in Endohedral TM@(ZnS)(12) structures, [EN] II−VI semiconducting materials are gaining attention due to their optoelectronic properties. Moreover, the addition of transition metals, TMs, might give them magnetic properties. The location and distance of the TM are crucial in determining such magnetic properties. In this work, we focus on small hollow (ZnS)12 nanoclusters doped with TMs. Because (ZnS)12 is a cage-like spheroid, the cavity inside the structure allows for the design of endohedral compounds resembling those of C60. Previous studies theoretically predicted that the first-row TM(ZnS)12 endohedral compounds were thermodynamically unstable compared to the surface compounds, where the TM atom is located at the surface of the cluster. The transition states connecting both structure families were calculated, and the estimated lifetimes of these compounds were predicted to be markedly small. However, in such works dispersion effects were not taken into account. Here, in order to check for the influence of dispersion on the possible stabilization of the desired TM(ZnS)12 endohedrally doped clusters, several functionals are tested and compare to MP2. It is found that the dispersion effects play a very important role in determining the location of the metals, especially in those TMs with the 4s3d shell half-filled or completely filled. In addition, a complete family of TM doped (ZnS)12 nanoclusters is explored using ab initio molecular dynamics simulations and local minima optimizations that could guide the experimental synthesis of such compounds. From the magnetic point of view, the Cr(7S)@(ZnS)12 compound is the most interesting case, since the endohedral isomer is predicted to be the global minimum. Moreover, molecular dynamics simulations show that when the Cr atom is located at the surface of the cluster, it spontaneously migrates toward the center of the cavity at room temperature.

This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.

Role of dispersion interactions in Endohedral TM@(ZnS)(12) structures, [EN] II−VI semiconducting materials are gaining attention due to their optoelectronic properties. Moreover, the addition of transition metals, TMs, might give them magnetic properties. The location and distance of the TM are crucial in determining such magnetic properties. In this work, we focus on small hollow (ZnS)12 nanoclusters doped with TMs. Because (ZnS)12 is a cage-like spheroid, the cavity inside the structure allows for the design of endohedral compounds resembling those of C60. Previous studies theoretically predicted that the first-row TM(ZnS)12 endohedral compounds were thermodynamically unstable compared to the surface compounds, where the TM atom is located at the surface of the cluster. The transition states connecting both structure families were calculated, and the estimated lifetimes of these compounds were predicted to be markedly small. However, in such works dispersion effects were not taken into account. Here, in order to check for the influence of dispersion on the possible stabilization of the desired TM(ZnS)12 endohedrally doped clusters, several functionals are tested and compare to MP2. It is found that the dispersion effects play a very important role in determining the location of the metals, especially in those TMs with the 4s3d shell half-filled or completely filled. In addition, a complete family of TM doped (ZnS)12 nanoclusters is explored using ab initio molecular dynamics simulations and local minima optimizations that could guide the experimental synthesis of such compounds. From the magnetic point of view, the Cr(7S)@(ZnS)12 compound is the most interesting case, since the endohedral isomer is predicted to be the global minimum. Moreover, molecular dynamics simulations show that when the Cr atom is located at the surface of the cluster, it spontaneously migrates toward the center of the cavity at room temperature.

Memory errors have been present in software for more than three decades now, leading to numerous security issues. Low-level languages like C and C++ that are prone to this class of errors are in widespread use, meaning that a large number of current systems are susceptible to attacks that target memory corruption vulnerabilities. Therefore, hardening programs and protecting against this type of attacks is of the utmost importance to build secure systems. This paper explains the basis of memory errors and their characteristics, analyzes the huge research effort that has been carried out in relation to memory error detection, exploitation and attack prevention, and depicts the most relevant approaches in this area.

Role of dispersion interactions in Endohedral TM@(ZnS)(12) structures, [EN] II−VI semiconducting materials are gaining attention due to their optoelectronic properties. Moreover, the addition of transition metals, TMs, might give them magnetic properties. The location and distance of the TM are crucial in determining such magnetic properties. In this work, we focus on small hollow (ZnS)12 nanoclusters doped with TMs. Because (ZnS)12 is a cage-like spheroid, the cavity inside the structure allows for the design of endohedral compounds resembling those of C60. Previous studies theoretically predicted that the first-row TM(ZnS)12 endohedral compounds were thermodynamically unstable compared to the surface compounds, where the TM atom is located at the surface of the cluster. The transition states connecting both structure families were calculated, and the estimated lifetimes of these compounds were predicted to be markedly small. However, in such works dispersion effects were not taken into account. Here, in order to check for the influence of dispersion on the possible stabilization of the desired TM(ZnS)12 endohedrally doped clusters, several functionals are tested and compare to MP2. It is found that the dispersion effects play a very important role in determining the location of the metals, especially in those TMs with the 4s3d shell half-filled or completely filled. In addition, a complete family of TM doped (ZnS)12 nanoclusters is explored using ab initio molecular dynamics simulations and local minima optimizations that could guide the experimental synthesis of such compounds. From the magnetic point of view, the Cr(7S)@(ZnS)12 compound is the most interesting case, since the endohedral isomer is predicted to be the global minimum. Moreover, molecular dynamics simulations show that when the Cr atom is located at the surface of the cluster, it spontaneously migrates toward the center of the cavity at room temperature.

This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.

Digitalizazioak oso bizkor aldatu izan du enpresa-jarduera. Maila meso-ekonomikoko analisi baten bidez, lan honek analizatzen du zer bilakaera izan duen euskal ekonomiaren digitalizazioak krisiaren ostean, sektore ekonomikoen intentsitate digitalari buruzko taxonomian oinarritutako esparru batetik abiatuta. Esparru horri, denbora-dimentsioa gehitu zaio, lagundu egiten baitu ulertzen zer jokaera izan duen hark denboran zehar. Emaitzak, berriz, erakusten du digitalizazioaren heterogeneotasuna dagoela euskal ekonomiaren jarduera-adarretan, bilakaera-eredu desberdinekin, eta adierazten du indartu behar direla identifikatutako jarduera digitalizatu ahulenetan., Digitization has been altering business activity at an accelerated pace. Using a mesoeconomic analysis, the paper analyses the evolution of digitization in the Basque economy after the crisis based on a taxonomy-based framework on the digital intensity of economic sectors. Time dimension has been added to this framework to understand its behavior over time. The result shows the existence of heterogeneity of digitization in the branches of activity of the Basque economy with different patterns in its evolution, and points to the need to reinforce the strategies of digitization of the weakest identified digitized activities., La digitalización ha venido alterando la actividad empresarial de manera acelerada. Mediante un análisis a nivel meso-económico, este trabajo analiza la evolución de la digitalización en la economía vasca tras la crisis a partir de un marco basado en taxonomía sobre la intensidad digital de los sectores económicos. A este marco se ha añadido la dimensión temporal que permite comprender su comportamiento a lo largo del tiempo. El resultado muestra la existencia de heterogeneidad de la digitalización en las ramas de actividad de la economía vasca con distintos patrones en su evolución, y señala la necesidad de reforzar las estrategias de digitalización de las actividades más débilmente digitalizadas identificadas.

Digitalizazioak oso bizkor aldatu izan du enpresa-jarduera. Maila meso-ekonomikoko analisi baten bidez, lan honek analizatzen du zer bilakaera izan duen euskal ekonomiaren digitalizazioak krisiaren ostean, sektore ekonomikoen intentsitate digitalari buruzko taxonomian oinarritutako esparru batetik abiatuta. Esparru horri, denbora-dimentsioa gehitu zaio, lagundu egiten baitu ulertzen zer jokaera izan duen hark denboran zehar. Emaitzak, berriz, erakusten du digitalizazioaren heterogeneotasuna dagoela euskal ekonomiaren jarduera-adarretan, bilakaera-eredu desberdinekin, eta adierazten du indartu behar direla identifikatutako jarduera digitalizatu ahulenetan., Digitization has been altering business activity at an accelerated pace. Using a mesoeconomic analysis, the paper analyses the evolution of digitization in the Basque economy after the crisis based on a taxonomy-based framework on the digital intensity of economic sectors. Time dimension has been added to this framework to understand its behavior over time. The result shows the existence of heterogeneity of digitization in the branches of activity of the Basque economy with different patterns in its evolution, and points to the need to reinforce the strategies of digitization of the weakest identified digitized activities., La digitalización ha venido alterando la actividad empresarial de manera acelerada. Mediante un análisis a nivel meso-económico, este trabajo analiza la evolución de la digitalización en la economía vasca tras la crisis a partir de un marco basado en taxonomía sobre la intensidad digital de los sectores económicos. A este marco se ha añadido la dimensión temporal que permite comprender su comportamiento a lo largo del tiempo. El resultado muestra la existencia de heterogeneidad de la digitalización en las ramas de actividad de la economía vasca con distintos patrones en su evolución, y señala la necesidad de reforzar las estrategias de digitalización de las actividades más débilmente digitalizadas identificadas.

Este trabajo nace bajo la motivación de la conservación del patrimonio no reconocido. El caso de estudio escogido es el Pabellón de los Hexágonos de la Casa de Campo de Madrid, ya que si bien su calidad en su ubicación actual ha sido cuestionada, en su origen en la Exposición universal de Bruselas de 1958 como Pabellón Español es incontestable. Siendo este el remanente de esa calidad arquitectónica que dotó a España en nuestro temprano modernismo, se hace un esfuerzo por recuperarlo. En este trabajo se elabora entonces un estudio histórico tanto del pabellón de Bruselas como de su traspaso posterior a Madrid y el nuevo proyecto en la Casa de Campo, que en la actualidad abandonado. Una vez conocidos los detalles del mismo a nivel teórico se comienza a recabar información de la situación actual del pabellón mediante visitas a terreno, en las que se mide y acota nuestro objeto. En estas visitas se elaboran una serie de croquis y se recogen fotografías de los daños, desperfectos y patologías que sufre el pabellón para una posterior clasificación y evaluación de los mismos. Para ello se lleva a cabo un estudio pormenorizado de los daños mediante fichas, en las que se descubren los daños, se evalúa su gravedad, se diagnostican sus causas y se proponen soluciones de ellas. Dado que la finalidad en sí misma no es rehabilitar, sino devolver una excelente pieza arquitectónica a la población se propone dotarla de un nuevo uso de museo, para el cual finalmente se elabora un pequeño proyecto hacia el que se afinan las decisiones de rehabilitación antes tomadas.

El programa desarrollado en el presente Proyecto de Fin de Grado, genera una ciudad a partir de los sistemas denominados L-systems. Es capaz de, definiendo una gramática, crear una red de carreteras que se asemeje a la de las ciudades reales. Una vez creada la red, es posible incluir edifi cios en las parcelas generadas por las diferentes secciones de las carreteras. Para la visualización de la ciudad se utiliza un motor grá fico provisto de las funciones básicas de renderizado: la visualización de objetos 3D, la cámara, texturas, ... Con esas herramientas es posible la visualización de los diferentes estilos de ciudad que se pueden generar mediante el programa.

El programa desarrollado en el presente Proyecto de Fin de Grado, genera una ciudad a partir de los sistemas denominados L-systems. Es capaz de, definiendo una gramática, crear una red de carreteras que se asemeje a la de las ciudades reales. Una vez creada la red, es posible incluir edifi cios en las parcelas generadas por las diferentes secciones de las carreteras. Para la visualización de la ciudad se utiliza un motor grá fico provisto de las funciones básicas de renderizado: la visualización de objetos 3D, la cámara, texturas, ... Con esas herramientas es posible la visualización de los diferentes estilos de ciudad que se pueden generar mediante el programa.

El programa desarrollado en el presente Proyecto de Fin de Grado, genera una ciudad a partir de los sistemas denominados L-systems. Es capaz de, definiendo una gramática, crear una red de carreteras que se asemeje a la de las ciudades reales. Una vez creada la red, es posible incluir edifi cios en las parcelas generadas por las diferentes secciones de las carreteras. Para la visualización de la ciudad se utiliza un motor grá fico provisto de las funciones básicas de renderizado: la visualización de objetos 3D, la cámara, texturas, ... Con esas herramientas es posible la visualización de los diferentes estilos de ciudad que se pueden generar mediante el programa.

Queueing networks describe complex stochastic systems of both theoretical and practical interest. They provide the means to assess alterations, diagnose poor performance and evaluate robustness across sets of interconnected resources. In the present paper, we focus on the underlying continuous-time Markov chains induced by these networks, and we present a flexible method for drawing parameter inference in multi-class Markovian cases with switching and different service disciplines. The approach is directed towards the inferential problem with missing data, where transition paths of individual tasks among the queues are often unknown. The paper introduces a slice sampling technique with mappings to the measurable space of task transitions between the service stations. This can address time and tractability issues in computational procedures, handle prior system knowledge and overcome common restrictions on service rates across existing inferential frameworks. Finally, the proposed algorithm is validated on synthetic data and applied to a real data set, obtained from a service delivery tasking tool implemented in two university hospitals.

Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures. Methods and Material: We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data. Results: Main results support claims relating to (i) the importance of data-driven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation. Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Electronic tasking technologies provide means to design intelligent systems specific to a ward, speciality or task-type; hence, the paper emphasizes the importance of replacing traditional pager-based approaches to management for modern alternatives.

In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets. Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

This paper studies stopping problems of the form $V=\inf_{0 \leq \tau \leq T} \mathbb{E}[U(\frac{\max_{0\le s \le T} Z_s }{Z_\tau})]$ for strictly concave or convex utility functions U in a family of increasing functions satisfying certain conditions, where Z is a geometric Brownian motion and T is the time of the nth jump of a Poisson process independent of Z. We obtain some properties of $V$ and offer solutions for the optimal strategies to follow. This provides us with a technique to build numerical approximations of stopping boundaries for the fixed terminal time optimal stopping problem presented in [J. Du Toit and G. Peskir, Ann. Appl. Probab., 19 (2009), pp. 983--1014].

This paper is concerned with analysing optimal wealth allocation techniques within a defaultable financial market similar to Bielecki and Jang (2007). It studies a portfolio optimization problem combining a continuous-time jump market and a defaultable security; and presents numerical solutions through the conversion into a Markov decision process and characterization of its value function as a unique fixed point to a contracting operator. This work analyses allocation strategies under several families of utilities functions, and highlights significant portfolio selection differences with previously reported results.

Queueing networks describe complex stochastic systems of both theoretical and practical interest. They provide the means to assess alterations, diagnose poor performance and evaluate robustness across sets of interconnected resources. In the present paper, we focus on the underlying continuous-time Markov chains induced by these networks, and we present a flexible method for drawing parameter inference in multi-class Markovian cases with switching and different service disciplines. The approach is directed towards the inferential problem with missing data, where transition paths of individual tasks among the queues are often unknown. The paper introduces a slice sampling technique with mappings to the measurable space of task transitions between the service stations. This can address time and tractability issues in computational procedures, handle prior system knowledge and overcome common restrictions on service rates across existing inferential frameworks. Finally, the proposed algorithm is validated on synthetic data and applied to a real data set, obtained from a service delivery tasking tool implemented in two university hospitals.

Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures. Methods and Material: We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data. Results: Main results support claims relating to (i) the importance of data-driven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation. Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Electronic tasking technologies provide means to design intelligent systems specific to a ward, speciality or task-type; hence, the paper emphasizes the importance of replacing traditional pager-based approaches to management for modern alternatives.

In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets. Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

This paper studies stopping problems of the form $V=\inf_{0 \leq \tau \leq T} \mathbb{E}[U(\frac{\max_{0\le s \le T} Z_s }{Z_\tau})]$ for strictly concave or convex utility functions U in a family of increasing functions satisfying certain conditions, where Z is a geometric Brownian motion and T is the time of the nth jump of a Poisson process independent of Z. We obtain some properties of $V$ and offer solutions for the optimal strategies to follow. This provides us with a technique to build numerical approximations of stopping boundaries for the fixed terminal time optimal stopping problem presented in [J. Du Toit and G. Peskir, Ann. Appl. Probab., 19 (2009), pp. 983--1014].

This paper is concerned with analysing optimal wealth allocation techniques within a defaultable financial market similar to Bielecki and Jang (2007). It studies a portfolio optimization problem combining a continuous-time jump market and a defaultable security; and presents numerical solutions through the conversion into a Markov decision process and characterization of its value function as a unique fixed point to a contracting operator. This work analyses allocation strategies under several families of utilities functions, and highlights significant portfolio selection differences with previously reported results.

This paper is concerned with analysing optimal wealth allocation techniques within a defaultable financial market similar to Bielecki and Jang (2007). It studies a portfolio optimization problem combining a continuous-time jump market and a defaultable security; and presents numerical solutions through the conversion into a Markov decision process and characterization of its value function as a unique fixed point to a contracting operator. This work analyses allocation strategies under several families of utilities functions, and highlights significant portfolio selection differences with previously reported results.

The following thesis is divided in two main topics. The first part studies variations of optimal prediction problems introduced in Shiryaev, Zhou and Xu (2008) and Du Toit and Peskir (2009) to a randomized terminal-time set up and different families of utility measures. The work presents optimal stopping rules that apply under different criteria, introduces a numerical technique to build approximations of stopping boundaries for fixed terminal time problems and suggest previously reported stopping rules extend to certain generalizations of measures. The second part of the thesis is concerned with analysing optimal wealth allocation techniques within a defaultable financial market similar to Bielecki and Jang (2007). It studies a portfolio optimization problem combining a continuous time jump market and a defaultable security; and presents numerical solutions through the conversion into a Markov Decision Process and characterization of its value function as a unique fixed point to a contracting operator. This work analyses allocation strategies under several families of utilities functions, and highlights significant portfolio selection differences with previously reported results.

Queueing networks describe complex stochastic systems of both theoretical and practical interest. They provide the means to assess alterations, diagnose poor performance and evaluate robustness across sets of interconnected resources. In the present paper, we focus on the underlying continuous-time Markov chains induced by these networks, and we present a flexible method for drawing parameter inference in multi-class Markovian cases with switching and different service disciplines. The approach is directed towards the inferential problem with missing data, where transition paths of individual tasks among the queues are often unknown. The paper introduces a slice sampling technique with mappings to the measurable space of task transitions between the service stations. This can address time and tractability issues in computational procedures, handle prior system knowledge and overcome common restrictions on service rates across existing inferential frameworks. Finally, the proposed algorithm is validated on synthetic data and applied to a real data set, obtained from a service delivery tasking tool implemented in two university hospitals.

Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures. Methods and Material: We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data. Results: Main results support claims relating to (i) the importance of data-driven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation. Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Electronic tasking technologies provide means to design intelligent systems specific to a ward, speciality or task-type; hence, the paper emphasizes the importance of replacing traditional pager-based approaches to management for modern alternatives.

In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets. Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

This paper studies stopping problems of the form $V=\inf_{0 \leq \tau \leq T} \mathbb{E}[U(\frac{\max_{0\le s \le T} Z_s }{Z_\tau})]$ for strictly concave or convex utility functions U in a family of increasing functions satisfying certain conditions, where Z is a geometric Brownian motion and T is the time of the nth jump of a Poisson process independent of Z. We obtain some properties of $V$ and offer solutions for the optimal strategies to follow. This provides us with a technique to build numerical approximations of stopping boundaries for the fixed terminal time optimal stopping problem presented in [J. Du Toit and G. Peskir, Ann. Appl. Probab., 19 (2009), pp. 983--1014].

This paper is concerned with analysing optimal wealth allocation techniques within a defaultable financial market similar to Bielecki and Jang (2007). It studies a portfolio optimization problem combining a continuous-time jump market and a defaultable security; and presents numerical solutions through the conversion into a Markov decision process and characterization of its value function as a unique fixed point to a contracting operator. This work analyses allocation strategies under several families of utilities functions, and highlights significant portfolio selection differences with previously reported results.

This paper is concerned with analysing optimal wealth allocation techniques within a defaultable financial market similar to Bielecki and Jang (2007). It studies a portfolio optimization problem combining a continuous-time jump market and a defaultable security; and presents numerical solutions through the conversion into a Markov decision process and characterization of its value function as a unique fixed point to a contracting operator. This work analyses allocation strategies under several families of utilities functions, and highlights significant portfolio selection differences with previously reported results.

The following thesis is divided in two main topics. The first part studies variations of optimal prediction problems introduced in Shiryaev, Zhou and Xu (2008) and Du Toit and Peskir (2009) to a randomized terminal-time set up and different families of utility measures. The work presents optimal stopping rules that apply under different criteria, introduces a numerical technique to build approximations of stopping boundaries for fixed terminal time problems and suggest previously reported stopping rules extend to certain generalizations of measures. The second part of the thesis is concerned with analysing optimal wealth allocation techniques within a defaultable financial market similar to Bielecki and Jang (2007). It studies a portfolio optimization problem combining a continuous time jump market and a defaultable security; and presents numerical solutions through the conversion into a Markov Decision Process and characterization of its value function as a unique fixed point to a contracting operator. This work analyses allocation strategies under several families of utilities functions, and highlights significant portfolio selection differences with previously reported results.

Queueing networks describe complex stochastic systems of both theoretical and practical interest. They provide the means to assess alterations, diagnose poor performance and evaluate robustness across sets of interconnected resources. In the present paper, we focus on the underlying continuous-time Markov chains induced by these networks, and we present a flexible method for drawing parameter inference in multi-class Markovian cases with switching and different service disciplines. The approach is directed towards the inferential problem with missing data, where transition paths of individual tasks among the queues are often unknown. The paper introduces a slice sampling technique with mappings to the measurable space of task transitions between the service stations. This can address time and tractability issues in computational procedures, handle prior system knowledge and overcome common restrictions on service rates across existing inferential frameworks. Finally, the proposed algorithm is validated on synthetic data and applied to a real data set, obtained from a service delivery tasking tool implemented in two university hospitals.

Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures. Methods and Material: We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data. Results: Main results support claims relating to (i) the importance of data-driven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation. Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Electronic tasking technologies provide means to design intelligent systems specific to a ward, speciality or task-type; hence, the paper emphasizes the importance of replacing traditional pager-based approaches to management for modern alternatives.

In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets. Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

This paper studies stopping problems of the form $V=\inf_{0 \leq \tau \leq T} \mathbb{E}[U(\frac{\max_{0\le s \le T} Z_s }{Z_\tau})]$ for strictly concave or convex utility functions U in a family of increasing functions satisfying certain conditions, where Z is a geometric Brownian motion and T is the time of the nth jump of a Poisson process independent of Z. We obtain some properties of $V$ and offer solutions for the optimal strategies to follow. This provides us with a technique to build numerical approximations of stopping boundaries for the fixed terminal time optimal stopping problem presented in [J. Du Toit and G. Peskir, Ann. Appl. Probab., 19 (2009), pp. 983--1014].

Queueing networks describe complex stochastic systems of both theoretical and practical interest. They provide the means to assess alterations, diagnose poor performance and evaluate robustness across sets of interconnected resources. In the present paper, we focus on the underlying continuous-time Markov chains induced by these networks, and we present a flexible method for drawing parameter inference in multi-class Markovian cases with switching and different service disciplines. The approach is directed towards the inferential problem with missing data, where transition paths of individual tasks among the queues are often unknown. The paper introduces a slice sampling technique with mappings to the measurable space of task transitions between the service stations. This can address time and tractability issues in computational procedures, handle prior system knowledge and overcome common restrictions on service rates across existing inferential frameworks. Finally, the proposed algorithm is validated on synthetic data and applied to a real data set, obtained from a service delivery tasking tool implemented in two university hospitals.

Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures. Methods and Material: We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data. Results: Main results support claims relating to (i) the importance of data-driven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation. Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Electronic tasking technologies provide means to design intelligent systems specific to a ward, speciality or task-type; hence, the paper emphasizes the importance of replacing traditional pager-based approaches to management for modern alternatives.

In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets. Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

This paper studies stopping problems of the form $V=\inf_{0 \leq \tau \leq T} \mathbb{E}[U(\frac{\max_{0\le s \le T} Z_s }{Z_\tau})]$ for strictly concave or convex utility functions U in a family of increasing functions satisfying certain conditions, where Z is a geometric Brownian motion and T is the time of the nth jump of a Poisson process independent of Z. We obtain some properties of $V$ and offer solutions for the optimal strategies to follow. This provides us with a technique to build numerical approximations of stopping boundaries for the fixed terminal time optimal stopping problem presented in [J. Du Toit and G. Peskir, Ann. Appl. Probab., 19 (2009), pp. 983--1014].

This paper is concerned with analysing optimal wealth allocation techniques within a defaultable financial market similar to Bielecki and Jang (2007). It studies a portfolio optimization problem combining a continuous-time jump market and a defaultable security; and presents numerical solutions through the conversion into a Markov decision process and characterization of its value function as a unique fixed point to a contracting operator. This work analyses allocation strategies under several families of utilities functions, and highlights significant portfolio selection differences with previously reported results.