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Data from three long-term field studies with loblolly pine (Pinus taeda L.) plantations were used to examine inequality (Gini coefficient) trends in diameter and the relationship between diameter relative growth rate (r) and initial size. Analysis with two spacing studies shows inequality increases with increasing density. For a given initial density, inequality initially decreases and then begins to increase as trees compete for resources. The slope of the linear relationship between r and relative size also increases with increasing density. The slope is initially negative and switches to positive as competition intensifies. The switch in the slope of the r/size relationship occurs when the crown projection area exceeds 1.05 or when the crown ratio falls below 0.75. These results are consistent with the resource pre-emptive or dominance/suppression theory of intra-specific competition. The r/size trends are not evident when calculations are based on class means as opposed to individual trees. The slope of the r/size relationship is a function of stand height, density, and to a lesser extent, site quality. Density reduction through mid-rotation thinning tends to decrease the slope coefficient. The r/size trends are used to develop a disaggregation model to distribute stand-level basal area growth over an initial tree list. This approach compares well with two other disaggregation models but tends to over predict growth on the largest trees. / Ph. D.

We study the resource investment decision faced by a firm that offers two demand-classes (i.e., products, services), while incorporating the firm's pricing decision into the investment decision. For this purpose, we consider a monopolistic situation and model the demand curve of each demand-class as a downward sloping linear function of its own price. The firm can invest in dedicated resources, which can only satisfy a specific demand-class, and/or in a more expensive, flexible resource, which can satisfy both demand-classes. We consider a two-stage stochastic decision model: In the first stage, the firm determines the dedicated and flexible resource capacities to invest in under demand uncertainty. In the second stage, demand curves are realized and the firm optimizes its revenue through pricing and resource allocation decisions, constrained by its capacity investment decision in the first stage. Our analysis provides the structure of the firm's optimal resource investment strategy as a function of price elasticities and investment costs, and shows how the value of resource flexibility depends on these parameters and demand correlations. Based on our analysis, we provide principles on the firm's optimal resource investment strategy under uncertainty. We show that it can be optimal for the firm to invest in the flexible resource when demand patterns are perfectly positively correlated, while it is not always optimal to invest in the flexible resource when demand patterns are perfectly negatively correlated. / Master of Science

This dissertation examines how trust develops in landscape level collaborative natural resource management efforts. I took a case study approach to research four projects in the Collaborative Forest Landscape Restoration Program (CFLRP). I conducted qualitative analysis of interviews, notes and observations from site visits and archival documents to understand the role and function of trust within my four case studies. The results are organized into five chapters: an introduction chapter, three manuscripts intended for stand-alone publication (Chapters 2-4), and a conclusion chapter. Chapter 2 reports on the development and function of trust within the four collaboratives I studied. Chapter 3 discusses the roles of facilitators and coordinators for engendering and maintaining trust. Chapter 4 examines three of the four case studies and compares the observed trust outcomes to the literature on traditional forms of public involvement. Finally, Chapter 5 synthesizes the findings from Chapters 2-4. These results provide useful information about the types of trust that contribute to successful collaborative efforts, as well as identify some of the practices and structures that engender those types of trust. / Ph. D.

Modern mobile communication systems will provide enhanced high-speed data, multimedia, and voice services to mobile users. The integration of such heterogeneous traffic types implies that the network must provide differentiated Quality of Service (QoS). Beam forming techniques have been proposed to increase the spectral efficiency of the wireless channel. Using beamforming in the network will lead to intra-cell handoffs within the cell due to user mobility. In a commercially deployed next generation cellular system, it is likely that beam forming and QoS guarantees to the users will co-exist. In this work we propose a resource allocation and management scheme tailored for a network that employs smart antennas in support of a heterogeneous user mix. Resource management in a wireless system should take care of channel impairments and non-ideal antenna patterns. Mobile users moving from one beam to the other give rise to resource reallocation issues. Depending on the scatterer distribution in the cell, the Angle of Arrival (AoA) of the users will also change, affecting the interference pattern in the cell. In a system with data and multimedia users, some of the users are likely to be elastic in their demands for bandwidth. In this work, we propose a resource allocation and management scheme tailored for systems with smart antennas having heterogeneous users. The algorithm works by comparing the received power in the beams. Elasticity of user requirement for data services is exploited to provide adaptive QoS, thereby reducing the call dropping probability due to user mobility. Simulation results showing the channel and Multiple Access Interference (MAI) effects on system performance are presented. The effect of channel coding to provide Bit Error Rate (BER) guarantees is studied. We also show the throughput advantage obtained using the resource management algorithms. It is also seen that the throughput of the system increases for a user population having a higher elasticity. A modified resource allocation algorithm to reduce the blocking probability of the calls is presented and performance verified using simulation. The probability of call dropping in an unmanaged system due to user mobility is shown. Our studies show that using managed system the call drop probability can be minimized. / Master of Science

<p> This dissertation tests two methods to obtain information of the distribution and movement of cetaceans. The first method uses vessels of opportunity as platforms to conduct acoustic surveys between the main Hawaiian Islands, with the ultimate goal of providing a method that can be used in future studies to contribute to mapping distribution and habitat modeling of data-poor cetacean species in the areas of the ocean which are infrequently surveyed. The distribution of a well-studied species, the humpback whale <i> Megaptera novaeangliae</i> was mapped and analyzed in relation to remotely sensed data on ocean depth, sea surface temperature, sea surface height, wind speed, chlorophyll-A, and surface currents. The results agreed with previous research on humpback whales, indicating that acoustic surveys from vessels of opportunity are a viable method for collecting distribution data on cetaceans. The predicted species of odontocete whistles collected during vessel of opportunity surveys was determined using the Real-time Odontocete Call Classification Algorithm, and analyzed in respect to remotely sensed data. The sighting rate for odontocete surveys in this study is comparable to that of previous survey methods, and cryptic species are identified at a higher relative rate than when using visual sighting methods. The biases inherent in concentrating survey effort primarily in the calm waters on the leeward sides the Hawaiian Islands are discussed, as well as the drawbacks of relying on visual sighting methods for detecting species with low visual detection probability. In addition, the potential impacts of anthropogenic noise and ship strikes from commercial vessels are discussed during the case study of a high-speed craft. This dissertation also discusses a second methodology involving the use of DIFAR sonobuoys to track multiple singing humpback whales, with the end goal of learning more about the function about humpback song. This method is also applicable to other cetacean species.</p>

Cloud computing has become a powerful enabler for many IT services and new technolo-gies. It provides access to an unprecedented amount of resources in a fine-grained andon-demand manner. To deliver such a service, cloud providers should be able to efficientlyand reliably manage their available resources. This becomes a challenge for the manage-ment system as it should handle a large number of heterogeneous resources under diverseworkloads with fluctuations. In addition, it should also satisfy multiple operational require-ments and management objectives in large scale data centers.Autonomic computing techniques can be used to tackle cloud resource managementproblems. An autonomic system comprises of a number of autonomic elements, which arecapable of automatically organizing and managing themselves rather than being managedby external controllers. Therefore, they are well suited for decentralized control, as theydo not rely on a centrally managed state. A decentralized autonomic system benefits fromparallelization of control, faster decisions and better scalability. They are also more reliableas a failure of one will not affect the operation of the others, while there is also a lower riskof having faulty behaviors on all the elements, all at once. All these features are essentialrequirements of an effective cloud resource management.This thesis investigates algorithms, models, and techniques to autonomously managejobs, services, and virtual resources in a cloud data center. We introduce a decentralizedresource management framework, that automates resource allocation optimization and ser-vice consolidation, reliably schedules jobs considering probabilistic failures, and dynam-icly scales and repacks services to achieve cost efficiency.As part of the framework, we introduce a decentralized scheduler that provides andmaintains durable allocations with low maintenance costs for data centers with dynamicworkloads. The scheduler assigns resources in response to virtual machine requests andmaintains the packing efficiency while taking into account migration costs, topologicalconstraints, and the risk of resource contention, as well as fluctuations of the backgroundload.We also introduce a scheduling algorithm that considers probabilistic failures as part ofthe planning for scheduling. The aim of the algorithm is to achieve an overall job reliabil-ity, in presence of correlated failures in a data center. To do so, we study the impacts ofstochastic and correlated failures on job reliability in a virtual data center. We specificallyfocus on correlated failures caused by power outages or failure of network components onjobs running large number of replicas of identical tasks.Additionally, we investigate the trade-offs between vertical and horizontal scaling. Theresult of the investigations is used to introduce a repacking technique to automatically man-age the capacity required by an elastic service. The repacking technique combines thebenefits of both scaling strategies to improve its cost-efficiency. / Datormoln har kommit att bli kraftfulla möjliggörare för många nya IT-tjänster. De ger tillgång till mycket storskaliga datorresurser på ett finkornigt och omedelbart sätt. För att tillhandahålla sådana resurser krävs att de underliggande datorcentren kan hantera sina resurser på ett tillförlitligt och effektivt sätt. Frågan hur man ska designa deras resurshanteringssystem är en stor utmaning då de ska kunna hantera mycket stora mängder heterogena resurser som i sin tur ska klara av vitt skilda typer av belastning, ofta med väldigt stora variationer över tid. Därtill ska de typiskt kunna möta en mängd olika krav och målsättningar för hur resurserna ska nyttjas. Autonomiska system kan med fördel användas för att realisera sådana system. Ett autonomt system innehåller ett antal autonoma element som automatiskt kan organisera och hantera sig själva utan stöd av externa regulatorer. Förmågan att hantera sig själva gör dem mycket lämpliga som komponenter i distribuerade system, vilka i sin tur kan bidra till snabbare beslutsprocesser, bättre skalbarhet och högre feltolerans. Denna avhandling fokuserar på algoritmer, modeller och tekniker för autonom hantering av jobb och virtuella resurser i datacenter. Vi introducerar ett decentraliserat resurshanteringssystem som automatiserar resursallokering och konsolidering, schedulerar jobb tillförlitligt med hänsyn till korrelerade fel, samt skalar resurser dynamiskt för att uppnå kostnadseffektivitet. Som en del av detta ramverk introducerar vi en decentraliserad schedulerare som allokerar resurser med hänsyn till att tagna beslut ska vara bra för lång tid och ge låga resurshanteringskostnader för datacenter med dynamisk belastning. Scheduleraren allokerar virtuella maskiner utifrån aktuell belastning och upprätthåller ett effektivt nyttjande av underliggande servrar genom att ta hänsyn till migrationskostnader, topologiska bivillkor och risk för överutnyttjande. Vi introducerar också en resursallokeringsalgoritm som tar hänsyn till korrelerade fel som ett led i planeringen. Avsikten är att kunna uppnå specificerade tillgänglighetskrav för enskilda tjänster trots uppkomst av korrelerade fel. Vi fokuserar främst på korrelerade fel som härrör från problem med elförsörjning och från felande nätverkskomponenter samt deras påverkan på jobb bestående av många identiska del-jobb. Slutligen studerar vi även hur man bäst ska kombinera horisontell och vertikal skalning av resurser. Resultatet är en process som ökar kostnadseffektivitet genom att kombinera de två metoderna och därtill emellanåt förändra fördelning av storlekar på virtuella maskiner.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / The Joint Mission Environment Test Capability (JMETC) is a distributed live, virtual, and constructive (LVC) testing capability developed to support the acquisition community and to demonstrate Net-Ready Key Performance Parameters (KPP) requirements in a customer-specific Joint Mission Environment (JME). JMETC, using the Test and Training Enabling Architecture (TENA), provides connectivity to the Services' distributed test capabilities and simulations, and Industry test resources. TENA is well-designed for supporting JMETC events through its architecture and software capabilities which enable interoperability among range instrumentation systems, facilities, and simulations. TENA, used in major exercises and distributed test events, is also interfacing with other emerging range systems.

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / The Joint Mission Environment Test Capability (JMETC) is a distributed live, virtual, and constructive (LVC) testing capability developed to support the acquisition community and to demonstrate Net-Ready Key Performance Parameters (KPP) requirements in a customer-specific Joint Mission Environment (JME). JMETC, using the Test and Training Enabling Architecture (TENA), provides connectivity to the Services' distributed test capabilities and simulations, and Industry test resources. TENA is well-designed for supporting JMETC events through its architecture and software capabilities which enable interoperability among range instrumentation systems, facilities, and simulations. TENA, used in major exercises and distributed test events, is also interfacing with other emerging range systems.

Subcontractors perform the majority of the work on commercial construction projects. However, only limited research has focused on subcontractors’ practical needs. In response, this research discovered and documented subcontractor needs through data collections and extended these findings with development of an information model and tool. This research significantly enlarges detailed understanding of subcontractor management practices, in particular around resource allocation across multiple projects. While existing research has shown the importance of multi-project management, exploration of details of this process has largely been limited to overviews of policies. Rich details that allow for specific critiques of existing methods and tools have been missing. This research provides such details, including specific limitations and recommendations to existing information standards, commercial applications, and assignment algorithms. In particular, contributions of this research focus on support for what-if analysis under extreme frequency of resource reallocation, a limitation of existing tools and methods that make them unsuitable for most subcontractors. / text