The Aesthetics of Motion in Musics for the Mevlana Celal ed-Din Rumi

This dissertation investigates the concept of motion as a fundamental aesthetic element in the devotional music, dance, and rituals performed in honor of the celebrated thirteenth-century Persian mystic poet and saint, the Mevlana Celal ed-Din Muhammad Rumi. The main focus of the study is threefold. First, it investigates the prevalence of the notion of movement in Islamic music and culture, specifically within the Sufi communities of Turkey, in order to arrive at a broader understanding of the relationship between music, aesthetics, and worldview. Secondly, it explores how musical performance functions as a form of devotion or religious worship by focusing on the musical repertories performed in honor of a single holy figure, the Mevlana Rumi. Finally, it provides an ethnographic account of contemporary developments in Sufi musical culture in Turkey and across the world by describing the recent activities of the Mevlana's devotees, which includes members of the Mevlevi Order of Islamic mystics as well as adherents of other Sufi brotherhoods and followers of so-called New Religions or New Age. The primary research for this study involved two short one-month field trips to Turkey and India in 2002 and 2003, respectively, and a longer one year expedition to Turkey in 2004 and 2005, which also included shorter stays in Cyprus, Syria, and Egypt. Additionally, the dissertation draws directly from critical theories advanced in the fields of ethnomusicology, cultural anthropology, and ethnochoreology and focuses on the kinesthetic parameters of music, dance, trance, and ritual as well as on broader forms of socio-cultural movement including pilgrimage, cultural tourism, and globalization. These forms of movement are analyzed in four broad categories of music used in worship, including classical Mevlevi music, music of the zikr ceremony, popular musics, and non-Turkish musics.

SOCIAL AND ECOLOGICAL FACTORS INFLUENCING COLLECTIVE BEHAVIOR IN GIANT DANIO

A fundamental problem in biology is understanding how and why things group together. Collective behavior is observed on all organismic levels - from cells and slime molds, to swarms of insects, flocks of birds, and schooling fish, and in mammals, including humans. The long-term goal of this research is to understand the functions and mechanisms underlying collective behavior in groups. This dissertation focuses on shoaling (aggregating) fish. Shoaling behaviors in fish confer foraging and anti-predator benefits through social cues from other individuals in the group. However, it is not fully understood what information individuals receive from one another or how this information is propagated throughout a group. It is also not fully understood how the environmental conditions and perturbations affect group behaviors. The specific research objective of this dissertation is to gain a better understanding of how certain social and environmental factors affect group behaviors in fish. I focus on two ecologically relevant decision-making behaviors: (i) rheotaxis, or orientation with respect to a flow, and (ii) startle response, a rapid response to a perceived threat. By integrating behavioral and engineering paradigms, I detail specifics of behavior in giant danio Devario aequipinnatus (McClelland 1893), and numerically analyze mathematical models that may be extended to group behavior for fish in general, and potentially other groups of animals as well. These models that predict behavior data, as well as generate additional, testable hypotheses. One of the primary goals of neuroethology is to study an organism's behavior in the context of evolution and ecology. Here, I focus on studying ecologically relevant behaviors in giant danio in order to better understand collective behavior in fish. The experiments in this dissertation provide contributions to fish ecology, collective behavior, and biologically-inspired robotics.

Vehicle Routing Problems that Minimize the Completion Time: Heuristics, Worst-Case Analyses, and Computational Results

In the standard Vehicle Routing Problem (VRP), we route a fleet of vehicles to deliver the demands of all customers such that the total distance traveled by the fleet is minimized. In this dissertation, we study variants of the VRP that minimize the completion time, i.e., we minimize the distance of the longest route. We call it the min-max objective function. In applications such as disaster relief efforts and military operations, the objective is often to finish the delivery or the task as soon as possible, not to plan routes with the minimum total distance. Even in commercial package delivery nowadays, companies are investing in new technologies to speed up delivery instead of focusing merely on the min-sum objective. In this dissertation, we compare the min-max and the standard (min-sum) objective functions in a worst-case analysis to show that the optimal solution with respect to one objective function can be very poor with respect to the other. The results motivate the design of algorithms specifically for the min-max objective. We study variants of min-max VRPs including one problem from the literature (the min-max Multi-Depot VRP) and two new problems (the min-max Split Delivery Multi-Depot VRP with Minimum Service Requirement and the min-max Close-Enough VRP). We develop heuristics to solve these three problems. We compare the results produced by our heuristics to the best-known solutions in the literature and find that our algorithms are effective. In the case where benchmark instances are not available, we generate instances whose near-optimal solutions can be estimated based on geometry. We formulate the Vehicle Routing Problem with Drones and carry out a theoretical analysis to show the maximum benefit from using drones in addition to trucks to reduce delivery time. The speed-up ratio depends on the number of drones loaded onto one truck and the speed of the drone relative to the speed of the truck.