Species Range Shifts in Dynamic Geological and Climatic Landscapes: Studies in Temperate and Tropical Trees

Spatial patterns in the geographic distributions of ecosystems, species, and genetic variation are the result of both ecological conditions and evolutionary dynamics that have unfolded in a long, historical process. Attempts to account for spatial patterns in biodiversity provided some of the earliest inspiration for the development of the theory of evolution, but more than a century and a half later, biologists are still discovering precisely how and why these patterns arise. As a field, historical biogeography has emphasized the importance of Earth’s geological and climatic history in understanding dynamic spatial biodiversity patterns. It is well known that species and populations respond to geological and climatic change, but the details about how these responses unfolded are often vaguely specified, and the precise ecological mechanisms mediating them are often unknown. My dissertation tests specific hypotheses about historical range shifts and their genetic consequences, using recent advances such as next-generation sequencing, demographic and coalescent modelling, Approximate Bayesian Computation, and large datasets of species occurrence records and species traits. This work spans several arboreal study systems, and aims to provide new insight into unanswered questions and controversial historical biogeographic hypotheses. I begin by considering the effects of glaciation on range shifts in two hickory species from eastern North America. It is not precisely known where most temperate deciduous tree species from this region survived the Last Glacial Maximum, and whether northern populations existed and contributed to postglacial recolonization. I show that both species were likely fairly geographically widespread and genetically connected, and that postglacial recolonization occurred from a northern source in one species and a southern source in another. Next, I develop and test competing hypotheses about the ecological factors mediating postglacial range shifts in canyon live oak from California. I aim to attain mechanistic insight into the drivers of historical range shifts in this geographically complex Mediterranean-climate region, and find that summer drought tolerance was likely a key ecological factor mediating these shifts. Lastly, I zoom out in scale and ask how long-term migrational responses to geological and climatic change affect the assembly of entire communities. I examine the biogeographic distributions of >1,000 Neotropical rainforest woody plant species, and show that traits such as drought tolerance and elevational range have impacted the ability of species to disperse across or around the biogeographic filter created by Pliocene uplift of the northern Andes and formation of dry habitats in northeastern South America. Overall, my dissertation work has revealed the importance of species-specific responses to geological and climatic change, and how these responses affect the geographic distribution of biodiversity (from genetic to species to community levels). I test hypotheses concerning the geographic locations from which range shifts occurred (in hickories), the ecological factors mediating range shifts in complex environments (in canyon live oak), and the community-wide impact of species traits on biogeographic dispersal (in Neotropical trees). This work contributes to a growing body of knowledge helping transform historical biogeography from a realm of broad patterns, into a field where new insight can be gained by accounting for species-specific histories and the ecological processes that mediate range shifts.PHDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144154/1/jbemmels_1.pd

A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation

The existing model predictive control algorithm based on continuous control using quadratic programming is currently one of the most used modern control strategies applied to wind turbines. However, heavy computational time involved and complexity in implementation are still obstructions in existing model predictive control algorithm. Owing to this, a new switched model predictive control technique is developed for the control of wind turbines with the ability to reduce complexity while maintaining better efficiency. The proposed technique combines model predictive control operating on finite control set and artificial intelligence with reinforcement techniques (Markov Chains, MC) to design a new effective control law which allows to achieve the control objectives in different wind speed zones with minimization of computational complexity. The proposed method is compared with the existing model predictive control algorithm, and it has been found that the proposed algorithm is better in terms of computational time, load mitigation, and dynamic response. The proposed research is a forward step towards refining modern control techniques to achieve optimization in nonlinear process control using novel hybrid structures based on conventional control laws and artificial intelligence.© 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)fi=vertaisarvioitu|en=peerReviewed

Multi-Factors Aware Dual-Attentional Knowledge Tracing

With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.Comment: Accepted by CIKM 2021, 10 pages, 10 figures, 6 table

Development of the future generation of smart high voltage connectors and related components for substations, with energy autonomy and wireless data transmission capability

The increased dependency on electricity of modern society, makes reliability of power transmission systems a key point. This goal can be achieved by continuously monitoring power grid parameters, so possible failure modes can be predicted beforehand. It can be done using existing Information and Communication Technologies (ICT) and Internet of Things (10T) technologies that include instrumentation and wireless communication systems, thus forming a wireless sensor network (WSN). Electrical connectors are among the most critical parts of any electrical system and hence, they can act as nodes of such WSN. Therefore, the fundamental objective of this thesis is the design, development and experimental validation of a self-powered IOT solution for real-time monitoring of the health status of a high-voltage substation connector and related components of the electrical substation. This new family of power connectors is called SmartConnector and incorporates a thermal energy harvesting system powering a microcontroller that controls a transmitter and several electronic sensors to measure the temperature, current and the electrical contact resistance (ECR) of the connector. These measurements are sent remotely via a Bluetooth 5 wireless communication module to a local gateway, which further transfers the measured data to a database server for storage as well as further analysis and visualization. By this way, after suitable data processing, the health status of the connector can be available in real-time, allowing different appealing functions, such as assessing the correct installation of the connector, the current health status or its remaining useful life (RUL) in real-time. The same principal can also be used for other components of substation like spacers, insulators, conductors, etc. Hence, to prove universality of this novel approach, a similar strategy is applied to a spacer which is capable of measuring uneven current distribution in three closely placed conductors. This novel IOT device is called as SmartSpacer. Care has to be taken that this technical and scientific development has to be compatible with existing substation bus bars and conductors, and especially to be compatible with the high operating voltages, i.e., from tens to hundreds of kilo-Volts (kV), and with currents in the order of some kilo-pm peres (kA). Although some electrical utilities and manufacturers have progressed in the development of such technologies, including smart meters and smart sensors, electrical device manufacturers such as of substation connectors manufacturers have not yet undertaken the technological advancement required for the development of such a new family of smart components involved in power transmission, which are designed to meet the future needs.La mayor dependencia de la electricidad de la sociedad moderna hace que la fiabilidad de los sistemas de transmisión de energía sea un punto clave. Este objetivo se puede lograr mediante la supervisión continua de los parámetros de la red eléctrica, por lo que los posibles modos de fallo se pueden predecir de antemano. Se puede hacer utilizando las tecnologías existentes de Tecnologías de la Información y la Comunicación (1CT) e Internet de las cosas (lo T) que incluyen sistemas de instrumentación y comunicación inalámbrica, formando así una red de sensores inalámbricos (WSN). Los conectores eléctricos se encuentran entre las partes más críticas de cualquier sistema eléctrico y, por lo tanto, pueden actuar como nodos de dicho VVSN. Por lo tanto, el objetivo fundamental de esta tesis es el diseño, desarrollo y validación experimental de una solución IOT autoalimentada para la supervisión en tiempo real del estado de salud de un conector de subestación de alta tensión y componentes relacionados de la subestación eléctrica. Esta nueva familia de conectores de alimentación se llama SmartConnector e incorpora un sistema de recolección de energía térmica que alimenta un microcontrolador que controla un transmisor y varios sensores electrónicos para medir la temperatura, la corriente y la resistencia del contacto eléctrico (ECR) del conector. Esta nueva familia de conectores de alimentación se llama SmartConnector e incorpora un sistema de recolección de energía térmica que alimenta un microcontrolador que controla un transmisor y varios sensores electrónicos para medir la temperatura, la corriente y la resistencia al contacto eléctrico (ECR) del conector. De esta manera, después del procesamiento de datos adecuado, el estado de salud del conector puede estar disponible en tiempo real, permitiendo diferentes funciones atractivas, como evaluar la correcta instalación del conector, el estado de salud actual o su vida útil restante (RUL) en tiempo real. El mismo principio también se puede utilizar para otros componentes de la subestación como espaciadores, aislantes, conductores, etc. Por lo tanto, para demostrar la universalidad de este enfoque novedoso, se aplica una estrategia similar a un espaciador, que es capaz de medir la distribución de corriente desigual en tres conductores estrechamente situados. Hay que tener cuidado de que este desarrollo técnico y científico tenga que sea compatible con las barras y "busbars" de subestación existentes, y sobre todo para ser compatible con las altas tensiones de funcionamiento, es decir, de decenas a cientos de kilovoltios (kV), y con corrientes en el orden de algunos kilo-Amperes (kA). Aunque algunas empresas eléctricas y fabricantes han progresado en el desarrollo de este tipo de tecnologías, incluidos medidores inteligentes y sensores inteligentes, los fabricantes de dispositivos eléctricos, como los fabricantes de conectores de subestación, aún no han emprendido el avance tecnológico necesario para el desarrollo de una nueva familia de componentes intel

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research