892 resultados para Large modeling projects


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Over the past few years, logging has evolved from from simple printf statements to more complex and widely used logging libraries. Today logging information is used to support various development activities such as fixing bugs, analyzing the results of load tests, monitoring performance and transferring knowledge. Recent research has examined how to improve logging practices by informing developers what to log and where to log. Furthermore, the strong dependence on logging has led to the development of logging libraries that have reduced the intricacies of logging, which has resulted in an abundance of log information. Two recent challenges have emerged as modern software systems start to treat logging as a core aspect of their software. In particular, 1) infrastructural challenges have emerged due to the plethora of logging libraries available today and 2) processing challenges have emerged due to the large number of log processing tools that ingest logs and produce useful information from them. In this thesis, we explore these two challenges. We first explore the infrastructural challenges that arise due to the plethora of logging libraries available today. As systems evolve, their logging infrastructure has to evolve (commonly this is done by migrating to new logging libraries). We explore logging library migrations within Apache Software Foundation (ASF) projects. We i find that close to 14% of the pro jects within the ASF migrate their logging libraries at least once. For processing challenges, we explore the different factors which can affect the likelihood of a logging statement changing in the future in four open source systems namely ActiveMQ, Camel, Cloudstack and Liferay. Such changes are likely to negatively impact the log processing tools that must be updated to accommodate such changes. We find that 20%-45% of the logging statements within the four systems are changed at least once. We construct random forest classifiers and Cox models to determine the likelihood of both just-introduced and long-lived logging statements changing in the future. We find that file ownership, developer experience, log density and SLOC are important factors in determining the stability of logging statements.

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Within Canada there are more than 2.5 million bundles of spent nuclear fuel with another approximately 2 million bundles to be generated in the future. Canada, and every country around the world that has taken a decision on management of spent nuclear fuel, has decided on long-term containment and isolation of the fuel within a deep geological repository. At depth, a deep geological repository consists of a network of placement rooms where the bundles will be located within a multi-layered system that incorporates engineered and natural barriers. The barriers will be placed in a complex thermal-hydraulic-mechanical-chemical-biological (THMCB) environment. A large database of material properties for all components in the repository are required to construct representative models. Within the repository, the sealing materials will experience elevated temperatures due to the thermal gradient produced by radioactive decay heat from the waste inside the container. Furthermore, high porewater pressure due to the depth of repository along with possibility of elevated salinity of groundwater would cause the bentonite-based materials to be under transient hydraulic conditions. Therefore it is crucial to characterize the sealing materials over a wide range of thermal-hydraulic conditions. A comprehensive experimental program has been conducted to measure properties (mainly focused on thermal properties) of all sealing materials involved in Mark II concept at plausible thermal-hydraulic conditions. The thermal response of Canada’s concept for a deep geological repository has been modelled using experimentally measured thermal properties. Plausible scenarios are defined and the effects of these scenarios are examined on the container surface temperature as well as the surrounding geosphere to assess whether they meet design criteria for the cases studied. The thermal response shows that if all the materials even being at dried condition, repository still performs acceptably as long as sealing materials remain in contact.

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The Pico de Navas landslide was a large-magnitude rotational movement, affecting 50x106m3 of hard to soft rocks. The objectives of this study were: (1) to characterize the landslide in terms of geology, geomorphological features and geotechnical parameters; and (2) to obtain an adequate geomechanical model to comprehensively explain its rupture, considering topographic, hydro-geological and geomechanical conditions. The rupture surface crossed, from top to bottom: (a) more than 200 m of limestone and clay units of the Upper Cretaceous, affected by faults; and (b) the Albian unit of Utrillas facies composed of silty sand with clay (Kaolinite) of the Lower Cretaceous. This sand played an important role in the basal failure of the slide due to the influence of fine particles (silt and clay), which comprised on average more than 70% of the sand, and the high content presence of kaolinite (>40%) in some beds. Its geotechnical parameters are: unit weight (δ) = 19-23 KN/m3; friction angle (φ) = 13º-38º and cohesion (c) = 10-48 KN/m2. Its microstructure consists of accumulations of kaolinite crystals stuck to terrigenous grains, making clayey peds. We hypothesize that the presence of these aggregates was the internal cause of fluidification of this layer once wet. Besides the faulted structure of the massif, other conditioning factors of the movement were: the large load of the upper limestone layers; high water table levels; high water pore pressure; and the loss of strength due to wet conditions. The 3D simulation of the stability conditions concurs with our hypothesis. The landslide occurred in the Recent or Middle Holocene, certainly before at least 500 BC and possibly during a wet climate period. Today, it appears to be inactive. This study helps to understand the frequent slope instabilities all along the Iberian Range when facies Utrillas is present.

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The water stored in and flowing through the subsurface is fundamental for sustaining human activities and needs, feeding water and its constituents to surface water bodies and supporting the functioning of their ecosystems. Quantifying the changes that affect the subsurface water is crucial for our understanding of its dynamics and changes driven by climate change and other changes in the landscape, such as in land-use and water-use. It is inherently difficult to directly measure soil moisture and groundwater levels over large spatial scales and long times. Models are therefore needed to capture the soil moisture and groundwater level dynamics over such large spatiotemporal scales. This thesis develops a modeling framework that allows for long-term catchment-scale screening of soil moisture and groundwater level changes. The novelty in this development resides in an explicit link drawn between catchment-scale hydroclimatic and soil hydraulics conditions, using observed runoff data as an approximation of soil water flux and accounting for the effects of snow storage-melting dynamics on that flux. Both past and future relative changes can be assessed by use of this modeling framework, with future change projections based on common climate model outputs. By direct model-observation comparison, the thesis shows that the developed modeling framework can reproduce the temporal variability of large-scale changes in soil water storage, as obtained from the GRACE satellite product, for most of 25 large study catchments around the world. Also compared with locally measured soil water content and groundwater level in 10 U.S. catchments, the modeling approach can reasonably well reproduce relative seasonal fluctuations around long-term average values. The developed modeling framework is further used to project soil moisture changes due to expected future climate change for 81 catchments around the world. The future soil moisture changes depend on the considered radiative forcing scenario (RCP) but are overall large for the occurrence frequency of dry and wet events and the inter-annual variability of seasonal soil moisture. These changes tend to be higher for the dry events and the dry season, respectively, than for the corresponding wet quantities, indicating increased drought risk for some parts of the world.

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Current research shows a relationship between healthcare architecture and patient-related Outcomes. The planning and designing of new healthcare environments is a complex process; the needs of the various end-users of the environment must be considered, including the patients, the patients’ significant others, and the staff. The aim of this study was to explore the experiences of healthcare professionals participating in group modelling utilizing system dynamics in the pre-design phase of new healthcare environments. We engaged healthcare professionals in a series of workshops using system dynamics to discuss the planning of healthcare environments in the beginning of a construction, and then interviewed them about their experience. An explorative and qualitative design was used to describe participants’ experiences of participating in the group modelling projects. Participants (n=20) were recruited from a larger intervention study using group modeling and system dynamics in planning and designing projects. The interviews were analysed by qualitative content analysis. Two themes were formed, representing the experiences in the group modeling process: ‘Partaking in the G-M created knowledge and empowerment’and ‘Partaking in the G-M was different from what was expected and required time and skills’. The method can support participants in design teams to focus more on their healthcare organization, their care activities and their aims rather than focusing on detailed layout solutions. This clarification is important when decisions about the design are discussed and prepared and will most likely lead to greater readiness for future building process.

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Structural Health Monitoring (SHM) is an emerging area of research associated to improvement of maintainability and the safety of aerospace, civil and mechanical infrastructures by means of monitoring and damage detection. Guided wave structural testing method is an approach for health monitoring of plate-like structures using smart material piezoelectric transducers. Among many kinds of transducers, the ones that have beam steering feature can perform more accurate surface interrogation. A frequency steerable acoustic transducer (FSATs) is capable of beam steering by varying the input frequency and consequently can detect and localize damage in structures. Guided wave inspection is typically performed through phased arrays which feature a large number of piezoelectric transducers, complexity and limitations. To overcome the weight penalty, the complex circuity and maintenance concern associated with wiring a large number of transducers, new FSATs are proposed that present inherent directional capabilities when generating and sensing elastic waves. The first generation of Spiral FSAT has two main limitations. First, waves are excited or sensed in one direction and in the opposite one (180 ̊ ambiguity) and second, just a relatively rude approximation of the desired directivity has been attained. Second generation of Spiral FSAT is proposed to overcome the first generation limitations. The importance of simulation tools becomes higher when a new idea is proposed and starts to be developed. The shaped transducer concept, especially the second generation of spiral FSAT is a novel idea in guided waves based of Structural Health Monitoring systems, hence finding a simulation tool is a necessity to develop various design aspects of this innovative transducer. In this work, the numerical simulation of the 1st and 2nd generations of Spiral FSAT has been conducted to prove the directional capability of excited guided waves through a plate-like structure.

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Thesis (Ph.D.)--University of Washington, 2016-08

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Thesis (Ph.D.)--University of Washington, 2016-08

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Thesis (Ph.D.)--University of Washington, 2016-08

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Variability management is one of the major challenges in software product line adoption, since it needs to be efficiently managed at various levels of the software product line development process (e.g., requirement analysis, design, implementation, etc.). One of the main challenges within variability management is the handling and effective visualization of large-scale (industry-size) models, which in many projects, can reach the order of thousands, along with the dependency relationships that exist among them. These have raised many concerns regarding the scalability of current variability management tools and techniques and their lack of industrial adoption. To address the scalability issues, this work employed a combination of quantitative and qualitative research methods to identify the reasons behind the limited scalability of existing variability management tools and techniques. In addition to producing a comprehensive catalogue of existing tools, the outcome form this stage helped understand the major limitations of existing tools. Based on the findings, a novel approach was created for managing variability that employed two main principles for supporting scalability. First, the separation-of-concerns principle was employed by creating multiple views of variability models to alleviate information overload. Second, hyperbolic trees were used to visualise models (compared to Euclidian space trees traditionally used). The result was an approach that can represent models encompassing hundreds of variability points and complex relationships. These concepts were demonstrated by implementing them in an existing variability management tool and using it to model a real-life product line with over a thousand variability points. Finally, in order to assess the work, an evaluation framework was designed based on various established usability assessment best practices and standards. The framework was then used with several case studies to benchmark the performance of this work against other existing tools.

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It is increasingly recognized that ecological restoration demands conservation action beyond the borders of existing protected areas. This requires the coordination of land uses and management over a larger area, usually with a range of partners, which presents novel institutional challenges for conservation planners. Interviews were undertaken with managers of a purposive sample of large-scale conservation areas in the UK. Interviews were open-ended and analyzed using standard qualitative methods. Results show a wide variety of organizations are involved in large-scale conservation projects, and that partnerships take time to create and demand resilience in the face of different organizational practices, staff turnover, and short-term funding. Successful partnerships with local communities depend on the establishment of trust and the availability of external funds to support conservation land uses. We conclude that there is no single institutional model for large-scale conservation: success depends on finding institutional strategies that secure long-term conservation outcomes, and ensure that conservation gains are not reversed when funding runs out, private owners change priorities, or land changes hands.

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When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.

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We present a detailed experimental study of the onset of rippling in highly crystalline carbon nanotubes. Modeling has shown that there should be a material constant, called the critical length, describing the dependence of the critical strain on the nanotube outer radius. Surprisingly, we have found very large variations, by a factor of three, in the critical length. We attribute this to a supporting effect from the inner walls in multiwalled concentric nanotubes. We provide an analytical expression for the maximum deflection prior to rippling, which is an important design consideration in nanoelectromechanical systems utilizing nanotubes.

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Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.

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Understanding how biodiversity spatially distribute over both the short term and long term, and what factors are affecting the distribution, are critical for modeling the spatial pattern of biodiversity as well as for promoting effective conservation planning and practices. This dissertation aims to examine factors that influence short-term and long-term avian distribution from the geographical sciences perspective. The research develops landscape level habitat metrics to characterize forest height heterogeneity and examines their efficacies in modelling avian richness at the continental scale. Two types of novel vegetation-height-structured habitat metrics are created based on second order texture algorithms and the concepts of patch-based habitat metrics. I correlate the height-structured metrics with the richness of different forest guilds, and also examine their efficacies in multivariate richness models. The results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of two forest bird guilds. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness. The second and the third projects focus on analyzing centroids of avian distributions, and testing hypotheses regarding the direction and speed of these shifts. I first showcase the usefulness of centroids analysis for characterizing the distribution changes of a few case study species. Applying the centroid method on 57 permanent resident bird species, I show that multi-directional distribution shifts occurred in large number of studied species. I also demonstrate, plain birds are not shifting their distribution faster than mountain birds, contrary to the prediction based on climate change velocity hypothesis. By modelling the abundance change rate at regional level, I show that extreme climate events and precipitation measures associate closely with some of the long-term distribution shifts. This dissertation improves our understanding on bird habitat characterization for species richness modelling, and expands our knowledge on how avian populations shifted their ranges in North America responding to changing environments in the past four decades. The results provide an important scientific foundation for more accurate predictive species distribution modeling in future.