The development of new and effective drugs is strongly affected by the need to identify drug targets and to reduce side effects. Unfortunately, resolving these issues depends partially on a broad and thorough understanding of the biological function of many proteins, and the experimental determination of protein function is expensive and time consuming. In response to this problem, algorithms for computational function prediction have been designed to expand experimental impact by finding proteins with predictably similar function, mapping experimental knowledge onto very similar, unstudied proteins. This thesis seeks to develop one method that can identify useful geometric and chemical similarities between well studied and unstudied proteins. Our approach is to identify matches of geometric and chemical similarity between motifs , representing known functional sites, and substructures of functionally uncharacterized proteins ( targets ). It is commonly hypothesized that the existence of a match could imply that the target contains an active site similar to the motif. We have designed the MASH ( M atch A ugmentation with S tatistical H ypothesis Testing) pipeline, a software tool for computing matches. MASH is the first method to match point-based motifs, developed in earlier work, that represent functional sites as points in space with ranked priorities and alternative chemical labels. MASH is also first to match cavity-aware motifs, a novel contribution of this work, that extend point-based motifs with volumetric information describing active clefts critical to protein function. Controlled experiments demonstrate that matches for both types of motifs can identify cognate active sites. However, motifs can also identify matches to functionally unrelated proteins. For this reason, we developed M otif Profiling (MP), the first method for motif refinement that reduces geometric similarity to functionally unre lated proteins. MP is implemented in two forms: Geometric Sieving (GS) refines point-based motifs and Cavity Scaling (CS) refines cavity-aware motifs. Controlled experimentation demonstrates that GS and CS identify motif refinements that have more matches to functionally related proteins and less matches to functionally unrelated proteins. This thesis demonstrates the importance of computational tools for matching and refining motifs, emphasizing the applicability of large-scale geometric and statistical analysis for functional annotation. / National Science Foundation, National Library of Medicine, AMD, Cray

The ability to visualize a solution space can be very beneficial, and it is generally accepted that the objective of visualization is to aid researchers in gathering insight. However, insight cannot be gathered effectively if the source data is misrepresented. This dissertation begins by demonstrating that the adaptive landscape visualization in widespread usage frequently misrepresents the neighborhood structure of genotypic space and, consequently, will mislead users about the manner in which solution space is traversed by the genetic algorithm. Bernhard Riemann, the father of topology, explicitly noted that a measurement of the distance between entities should represent the manner in which one can be brought towards the other. Thus, the commonly used Hamming distance, for example, is not representative of traversals of genotypic space by the genetic algorithm – a representative measure must include consideration for both mutation and recombination. This dissertation separately explores the properties that mutational and recombinational distances should have, and ultimately establishes a measure that is representative of the traversals made by both operators simultaneously. It follows that these measures can be used to enhance the adaptive landscape, by minimizing the discrepancy between the interpoint distances in genotypic space and the interpoint distances in the two-dimensional representation from which the landscape is extruded. This research also establishes a methodology for evaluating measures defining neighbourhood structures that are purportedly representative of traversals of genotypic space, by comparing them against an empirically generated norm. Through this approach it is conclusively demonstrated that the Hamming distance between genotypes is less representative than the proposed measures, and should not be used to define the neighbourhood structure from which visualizations would be constructed. While the proposed measures do not distort the data or otherwise mislead the user, they do require a significant computational expense. Fortunately, the choice to use these measures is always made at the discretion of the user, with additional costs incurred when accuracy and representativity are of paramount importance. These measures will ultimately find further application in population diversity measurement, cluster analysis, and any other task where the representativity of the neighborhood structure of the genotypic space is vital.

No description available.

Secondary (recycled) aluminum constitutes around 48% of the total aluminum used in the United States. Secondary aluminum melting is accomplished in large reverberatory furnaces, and improving its energy efficiency has been one of the major interests to aluminum industries. To assist the industries in improving energy efficiency in aluminum melting, an experimental research furnace (ERF), with 907 kg (2000 lbs) capacity, has been built at the Albany Research Center of the U.S. Department of Energy as part of this multi-partner research program. To verify that the experimental results obtained in the ERF furnace are valid for the operation of industrial furnaces, we used scale modeling technology to assist the validation. In this thesis, scaling laws, which are applied to the thermal conduction loss through the model furnace, were developed and the partial modeling relaxation technique was applied to the development of modeling to derive achievable scaling laws. The model experiments were conducted in the model furnace, which was a one-fourth scaled-down version from the ERF furnace (as a prototype), and then compared to the tests in the ERF furnace. The temperature distributions across both the model and prototype were shown to be in good agreement. Confirmation of the scaling laws demonstrated the usefulness of the scale modeling concept and its applicability to analyze complex melting processes in aluminum melting.

No description available.

No description available.

The increasingly diverse nature of modern applications makes it critical for future systems to have dynamic resource scaling capabilities which enable them to adapt their resource usage to meet user requirements. Such mechanisms should be both fine-grained in nature for resource-efficient operation and also provide a high scaling range to support a variety of applications with diverse needs. To this end, heterogeneous platforms, consisting of components with varying characteristics, have been proposed to provide improved performance/efficiency than homogeneous configurations, by making it possible to execute applications on the most suitable component. However, introduction of such heterogeneous architectural components requires system software to embrace complexity associated with heterogeneity for managing them efficiently. Diversity across vendors and rapidly changing hardware make it difficult to incorporate heterogeneity-aware resource management mechanisms into mainstream systems, affecting the widespread adoption of these platforms. Addressing these issues, this dissertation presents novel abstractions and mechanisms for heterogeneous platforms which decouple heterogeneity from management operations by masking the differences due to heterogeneity from applications. By exporting a homogeneous interface over heterogeneous components, it proposes the scalable 'resource state' abstraction, allowing applications to express their resource requirements which then are dynamically and transparently mapped to heterogeneous resources underneath. The proposed approach is explored for both modern mobile devices where power is a key resource and for cloud computing environments where platform resource usage has monetary implications, resulting in HeteroMates and HeteroVisor solutions. In addition, it also highlights the need for hardware and system software to consider multiple resources together to obtain desirable gains from such scaling mechanisms. The solutions presented in this dissertation open ways for utilizing future heterogeneous platforms to provide on-demand performance, as well as resource-efficient operation, without disrupting the existing software stack.

Purpose To investigate ocular surface sensations, specifically ocular discomfort using psychophysical and clinical techniques. The measurement of discomfort on the ocular surface has been limited to the use of traditional rating scales until recently. This thesis focuses on the scaling of discomfort using a psychophysical approach and also investigates the less explored area of the influence of blur on ocular discomfort. The specific aims of each chapter are: Chapter 2: To evaluate the difference thresholds of the central cornea in lens and non-lens wearers. Chapter 3: To devise a novel scale for ocular discomfort, relating subjective estimation of discomfort arising from contact lens wear to discomfort produced by the pneumatic stimuli delivered by a modified Belmonte esthesiometer. Chapter 4: To evaluate the influence of blur on ocular comfort while systematically manipulating vision using habitual refractive correction, induced spatial and optical blur, and under the absence of visual structure. Chapter 5: To examine if subjects rate discomfort and intensity of suprathreshold pneumatic stimuli differently when viewing clear and defocused targets and to examine the suprathreshold scaling of stimuli under the same visual conditions. Methods Chapter 2: The mechanical sensitivity of the central cornea was determined in 12 lens wearers and 12 non-lens wearers using a modified Belmonte pneumatic esthesiometer. The mechanical threshold of the central cornea was first estimated using the method of limits. Then, a series of systematically increasing stimuli were presented, with the first stimuli being 25% less than the threshold. The subjects were asked to compare the intensity of each stimulus with the preceding one and report if any difference in intensity was detectable. The intensities at which the subjects perceived an increased intensity from the previous was recorded. The difference threshold (DL) was the differences between the stimulus intensities at which an increase was perceived and five DLs were measured for each subject. Weber’s constants that relate the size of the difference thresholds to the stimulus intensity were derived for each DL level and repeated measures ANOVA was used to compare the Weber’s constants in the lens and non-lens wearing groups. Chapter 3: Twenty seven participants were enrolled for this magnitude matching study. Soft (HEMA) contact lenses of eight different lens designs varying in base curve and diameter were fit on all participants. The study was conducted on two separate days with four lenses randomly assigned on each day. The assigned soft contact lens was placed on the chosen eye and the sensations were measured using a numerical rating scale. Following this, the subjects were asked to regulate the intensity of the pneumatic stimulus using the control dial in order to match the discomfort from the stimulus to the discomfort from contact lens wear. At the completion of magnitude matching, ratings of sensations were again recorded. Pearson product moment correlation was used to correlate the objective esthesiometer matches to the subjective ratings of discomfort reported by each participant. The method of least log squares was used to derive the power exponents as defined by Stevens’ power law and analyze the psychophysical functions. Repeated measures ANOVA was used to investigate the effect of lens sequence and session on ocular discomfort with contact lens wear. The impact of lens type and time on discomfort was studied using linear mixed modeling. Chapter 4: Twenty emmetropic subjects rated ocular comfort, vision and sensation attributes (burning, itching and warmth) under conditions of normal vision, spatial blur and dioptric defocus, each session lasting for five minutes. Subjects viewed digital targets projected from a distance of 3m, and ocular surface sensations, vision were rated using magnitude estimation. Dioptric defocus was produced using +6.00DS contact lenses and equivalent spatial blur was created by spatially blurring the targets. Clear target images were used during dioptric defocus and blurred images during spatial blur session. Comfort was also rated under the absence of visual structure in fifteen of the participants using a ganzfeld and black occluders. Repeated measures ANOVA was used to compare vision and comfort ratings between the different experimental conditions. Chapter 5: Twenty one participants were enrolled. Ocular discomfort was produced by delivering mechanical stimuli from a pneumatic esthesiometer, and participants were asked to rate the intensity of stimulus and the discomfort induced by it under clear and defocused visual conditions. Esthesiometry was performed on one eye while the fellow eye viewed either a clear or blurred 6/60 fixation target through a trial lens. For the clear visual condition, the trial lens contained +0.25DS over their distance refractive correction and for the defocused condition, an additional +4.00DS was used. Mechanical thresholds from the central cornea were estimated using ascending methods of limits and then stimuli that were 25%, 50%, 75% and 100 % above threshold were presented in random order. Participants rated intensity and discomfort of each stimulus using a 0-100 numerical scale where 0 indicated no sensation and 100 indicated highest imaginable intensity/discomfort. There were 3 sessions with clear visual conditions and 3 sessions with defocus, in random order. Results Chapter 2: The functions relating Weber’s constants to stimulus intensities were slightly different in lens and non-lens wearing groups, although the absolute thresholds were similar. Repeated measures ANOVA revealed a significant main effect of DL level on Weber’s constant (p<0.001), with the Weber’s fraction at the first DL being higher than the following DLs. A significant main effect of the group type was also observed, with the lens wearers showing higher Weber’s constants than the non-lens wearers (p=0.02) However, there was no interaction between DL level and lens wearing group on Weber’s constants (p=0.38). Chapter 3: The average and individual psychophysical functions appeared to follow Stevens’ power function, with mechanical and chemical stimuli giving rise to different power exponents. Examination of the individual transducer functions revealed that only about half of the subjects were able to match the contact lens sensations to the pneumatic stimulus discomfort, with both mechanical and chemical stimulation. The lens types did not have any impact (p=0.65) on the session or sequence in which the lens was presented, although an effect of session and sequence on discomfort was observed. The average discomfort ratings produced by the different lens types were similar. There appeared to be significant effects of time (p<0.001) on the reporting of discomfort with lens wear, with the discomfort upon lens insertion rated to be higher than after lenses settling. Chapter 4: Ratings of vision under spatial blur and dioptric defocus were significantly different (p<0.001) from normal vision condition. Vision with dioptric defocus was rated worse (p<0.001) than spatial blur. Significant differences in comfort were observed between normal vision and blur, including spatial blur (p=0.02) and dioptric defocus (p=0.001). However, there was no significant difference (p=0.99) in comfort between spatial blur and dioptric defocus. Comfort remained unchanged between normal vision, occluders and ganzfeld although vision was absent in the later two conditions. Chapter 5: There was no significant difference in mechanical thresholds under clear and defocused conditions with a paired t-test (p=0.66) and similar results were obtained with repeated measures ANOVA, with no significant difference in discomfort (p=0.10) and intensity (p=0.075) ratings between the two visual conditions. However, paired t-test between the derived exponents under clear and defocused conditions showed significant differences for discomfort (p=0.05) and no significant difference for the ratings of intensity (p=0.22). Comparison of exponents between discomfort and intensity showed a significant difference in both clear (p=0.02) and defocus conditions (p<0.001). Conclusions: Chapter 2: The differential sensitivity of the ocular surface can be successfully measured with a pneumatic esthesiometer and it appears that Weber’s law holds true for corneal nociceptive sensory processing. There are subtle differences in mechanical difference thresholds between lens and non-lens wearers suggesting the possibility of different neural activity levels in the two groups. Chapter 3: Subjective ratings of discomfort can be scaled by corneal esthesiometry in a selective group of people. In the subset of subjects with poorer correlations, perhaps the pneumatic mechanical stimulus was too localized and specific to match the complex sensations experienced while wearing contact lenses. However, there is also a group of subjects who are poor at making judgments about ocular comfort. Hence, the use of special sensory panels should be considered when ocular comfort is the primary outcome. Chapter 4: There does seem to be an association between clarity of vision and ocular comfort, although the pathways for pain and vision are perhaps exclusive. Interactions between vision and other senses have been reported, but a similar inter-sensory interaction between pain and vision is yet to be clearly demonstrated. The decreased comfort observed in this study might perhaps be due to nocebo or Hawthorne effects. Chapter 5: Suprathreshold scaling of pneumatic stimuli can vary with the viewing conditions, with defocus associated with higher exponents than clear visual conditions. However, the ratings of comfort appear to be similar under both the conditions. If defocus does affect comfort, it is subtle and does not affect the sensory components, but tiny effects through the affective aspect of pain can contribute to the differences in power exponents. The differences in the perception of comfort do not appear to be attributable to the differences in threshold or sensory intensity.

Energy is a critical resource in all types of computing systems from servers, where energy costs dominate data centre expenses and carbon footprints, to embedded systems, where the system's battery life limits the device's functionality. In their efforts to reduce the energy use of these system's hardware manufacturers have implemented features which allow a reduced energy consumption under software control. This thesis shows that managing these settings is a more complex problem than previously considered. Where much (but not all) of the previous academic research investigates unrealistic scenarios, this thesis presents a solution to managing the power on varying hardware. Instead of making unrealistic assumptions, we extract a model from empirical data and characterise that model. Our models estimate the effect of different power management settings on the behaviour of the hardware platform, taking into account the workload, platform and environmental characteristics, but without any kind of a-priori knowledge of the specific workloads being run. These models encapsulate a system's knowledge of the platform. We also developed a \emph{generalised energy-delay} policy which allows us to quickly express the instantaneous importance of both performance and energy to the system. It allows us to select a power management strategy from a number of options. This thesis shows, by evaluation on a number of platforms, that our implementation, Koala, can accurately meet energy and performance goals. In some cases, our system saves 26\% of the system-level energy required for a task, while losing only 1\% performance. This is nearly 46\% of the dynamic energy. Taking advantage of all energy-saving opportunities requires detailed platform, workload and environmental information. Given this knowledge, we reach the exciting conclusion that near optimal power management is possible on real operating systems, with real platforms and real workloads.

Energy is a critical resource in all types of computing systems from servers, where energy costs dominate data centre expenses and carbon footprints, to embedded systems, where the system's battery life limits the device's functionality. In their efforts to reduce the energy use of these system's hardware manufacturers have implemented features which allow a reduced energy consumption under software control. This thesis shows that managing these settings is a more complex problem than previously considered. Where much (but not all) of the previous academic research investigates unrealistic scenarios, this thesis presents a solution to managing the power on varying hardware. Instead of making unrealistic assumptions, we extract a model from empirical data and characterise that model. Our models estimate the effect of different power management settings on the behaviour of the hardware platform, taking into account the workload, platform and environmental characteristics, but without any kind of a-priori knowledge of the specific workloads being run. These models encapsulate a system's knowledge of the platform. We also developed a \emph{generalised energy-delay} policy which allows us to quickly express the instantaneous importance of both performance and energy to the system. It allows us to select a power management strategy from a number of options. This thesis shows, by evaluation on a number of platforms, that our implementation, Koala, can accurately meet energy and performance goals. In some cases, our system saves 26\% of the system-level energy required for a task, while losing only 1\% performance. This is nearly 46\% of the dynamic energy. Taking advantage of all energy-saving opportunities requires detailed platform, workload and environmental information. Given this knowledge, we reach the exciting conclusion that near optimal power management is possible on real operating systems, with real platforms and real workloads.