Indirect estimation of semiparametric binary choice models*

One of the most cited studies within the field of binary choice models is that of Klein and Spady (1993), in which the authors propose a semiparametric estimator for use when the distribution of the error term is unknown. However, although theoretically appealing, the estimator has been found to be difficult to implement, and therefore not very attractive from an applied point of view. The current study offers an indirect inference-based solution to this problem. The new estimator is not only simple with good small-sample properties, but also consistent and asymptotically normal.

Predicting migratory flight altitudes by physiological migration models

Using the altitudinal profiles of wind, temperature, pressure, and humidity in three flight models, we tried to explain the altitudinal distributions of nocturnal migrants recorded by radar above a desert in southern Israel. In the simplest model, only the tailwind component was used as a predictor of the most preferred flight altitude (T model). The energy model (E model) predicted flight ranges according to mechanical power consumption in flapping flight depending on air density and wind conditions, assuming optimal adjustment of airspeed and compensation of crosswinds, and including the influence of mass loss during flight. The energy-water model (EW model) used the same assumptions and parameters as the E model but also included restrictions caused by dehydration. Because wind was by far the most important factor governing altitudinal distribution of nocturnal migrants, differences in predictions of the three models were small. In a first approach, the EW model performed slightly better than the E model, and both performed slightly better than the T model. Differences were most pronounced in spring, when migrants should fly high according to wind conditions, but when climbing and descending they must cross lower altitudes where conditions are better with respect to dehydration. A simplified energy model (Es model) that omits the effect of air density on flight costs explained the same amount of variance in flight altitude as the more complicated E and EW models. By omitting the effect of air density, the Es model predicted lower flight altitudes and thus compensated for factors that generally bias height distributions downward but are not considered in the models (i.e. climb and descent through lower air layers, cost of ascent, and decrease of oxygen partial pressure with altitude). Our results confirm that wind profiles, and thus energy rather than water limitations, govern the altitudinal distribution of nocturnal migrants, even under the extreme humidity and temperature conditions in the trade wind zone.

Stochastic optimization models for energy management in carbonization process of carbon fiber production

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Resource distribution influences positive edge effects in a seagrass fish

According to conceptual models, the distribution of resources plays a critical role in determining how organisms distribute themselves near habitat edges. These models are frequently used to achieve a mechanistic understanding of edge effects, but because they are based predominantly on correlative studies, there is need for a demonstration of causality, which is best done through experimentation. Using artificial seagrass habitat as an experimental system, we determined a likely mechanism underpinning edge effects in a seagrass fish. To test for edge effects, we measured fish abundance at edges (0-0.5 m) and interiors (0.5-1 m) of two patch configurations: continuous (single, continuous 9-m2 patches) and patchy (four discrete 1-m2 patches within a 9-m2 area). In continuous configurations, pipefish (Stigmatopora argus) were three times more abundant at edges than interiors (positive edge effect), but in patchy configurations there was no difference. The lack of edge effect in patchy configurations might be because patchy seagrass consisted entirely of edge habitat. We then used two approaches to test whether observed edge effects in continuous configurations were caused by increased availability of food at edges. First, we estimated the abundance of the major prey of pipefish, small crustaceans, across continuous seagrass configurations. Crustacean abundances were highest at seagrass edges, where they were 16% greater than in patch interiors. Second, we supplemented interiors of continuous treatment patches with live crustaceans, while control patches were supplemented with seawater. After five hours of supplementation, numbers of pipefish were similar between edges and interiors of treatment patches, while the strong edge effects were maintained in controls. This indicated that fish were moving from patch edges to interiors in response to food supplementation. These approaches strongly suggest that a numerically dominant fish species is more abundant at seagrass edges due to greater food availability, and provide experimental support for the resource distribution model as an explanation for edge effects.

Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.

Selecting umbrella species for conservation: a test of habitat models and niche overlap for beach-nesting birds

Umbrella species are rarely selected systematically from a range of candidate species. On sandy beaches, birds that nest on the upper beach or in dunes are threatened globally and hence are prime candidates for conservation intervention and putative umbrella species status. Here we use a maximum-likelihood, multi-species distribution modeling approach to select an appropriate conservation umbrella from a group of candidate species occupying similar habitats. We identify overlap in spatial extent and niche characteristics among four beach-nesting bird species of conservation concern, American oystercatchers (Haematopus palliatus), black skimmers (Rynchops niger), least terns (Sterna antillarum) and piping plovers (Charadrius melodus), across their entire breeding range in New Jersey, USA. We quantify the benefit and efficiency of using each species as a candidate umbrella on the remaining group. Piping plover nesting habitat encompassed 86% of the least tern habitat but only 15% and 13% of the black skimmer and American oystercatcher habitat, respectively. However, plovers co-occur with all three species across 66% of their total nesting habitat extent (~ 649 ha), suggesting their value as an umbrella at the local scale. American oystercatcher nesting habitat covers 100%, 99% and 47% of piping plover, least tern and black skimmer habitat, making this species more appropriate conservation umbrellas at a regional scale. Our results demonstrate that the choice of umbrella species requires explicit consideration of spatial scale and an understanding of the habitat attributes that an umbrella species represents and to which extent it encompasses other species of conservation interest. Notwithstanding the attractiveness of the umbrella species concept, local conservation interventions especially for breeding individuals in small populations may still be needed.

GIS and remote sensing-based spatial models for commercial fisheries assessment

By applying a novel set of multidisciplinary GIS, remote sensing and spatial modelling approaches, research presented in this thesis advances our knowledge of the distribution patterns, fishery and ecological status of an important commercial benthic macro-invertebrate, blacklip abalone, in south-east Australia.