No description available.

Visualization can be an effective way to explore and understand abstract data. Due to the rapidly changing technological environment of sys admin work and the scale of data involved, enhanced visualizations might provide benefit in this domain; however, despite research efforts, to-date the tools for system administrators (sys admins) minimally employ the use of interactivity in models and provide limited visualizations in tools. This may be because sys admins have a culture of command-line interface (CLI) use that is at odds to the graphical user interface (GUI) that accompanies most tools that incorporate interactive visualizations. We designed a two phase study to gain a better understanding about the work of sys admins, their current tool environment, their preferences for CLI and GUI based tools, and their perspective about how the inclusion of interactive visualizations in tools and system models might enhance their routines. The first phase of contextual inquiries and semi-structured interviews with 37 participants gave us a rich understanding of system admin work practices and their desired functionality for future tools. In the second phase, an on-line survey with 331 sys admins allowed us to generalize our findings. Based on our research, we generated recommendations for desired tool features in each of the sub-domains of sys admin work (i.e., network, virtualization etc.,). We also conducted an analysis of the type of visualizations that could be implemented in future tools to support the challenging nature of sys admin work.

The purpose of the present research study was to produce a global, cumulative model of number concept development for children between the ages of two and eight years old. The theoretical and methodological orientation of this study was greatly influenced by Richard Young's production system analysis of seriation by young children (Young, 1971, 1976) and by Newell's (1973) seminal paper, ‘You can't play twenty questions with nature and win’. The methodology used in this investigation thus was as follows. A series of complex number tasks encompassing many aspects of the concept of number were developed. Five children aged between three and seven years then were videotaped while performing some of these complex number tasks. From a detailed protocol analysis of the video-recordings, computer simulation models written in the production system language PSS3 (Ohlsson, 1979) were produced. Specific production system models were produced for each of following aspects of the children's number knowledge: (i) sharing of discrete quantities; (ii) comparison of shares; and (iii) conservation/addition/subtraction of number. These domain-specific models were based on the converging experimental evidence obtained from each of the children’s responses to variants of the complex number tasks. Each child thus received a different set of problems which were chosen systematically in order to clarify particular features of the child's abilities. After a production system model for each child had been produced within a domain, these models were compared and contrasted. From this analysis, developmental trends within the domain were identified and discussed. The research and educational implications of these developmental trends then were discussed. In the concluding parts of this study, the children's domain-specific production system models were cumulated into global, comprehensive models which accurately represented their behaviour in a variety of number tasks. These comprehensive models were compared and contrasted and general developmental trends in young children's number knowledge were identified and discussed.

Theories that formally describe decision-making have traditionally posited that decisions are made by rational actors. However, it is generally accepted that humans often make irrational decisions because of quick emotional judgements. In order to reconcile these two inconsistent ideas psychologists have developed two-system theories that hypothesize decisions are made by two opposing cognitive systems, representing the rational and emotional processing of decisions. Evidence for a two-system model of decision-making can be observed in ultimatum game responder decisions. It is thought that rational processing of these choices will produce acceptance of unfair offers and emotional processing will encourage rejection of unfair offers. Emotional priming has been shown to decrease ultimatum game acceptances and trans-cranial magnetic stimulation of rational brain areas, i.e. DLPFC, show increases in ultimatum game acceptances. This study investigated the possibility of using behavioral tasks that are known to activate rational brain areas to promote/disrupt ultimatum game acceptances. The possible relationship between ultimatum game acceptances and executive functions was also examined. Although there were promising indications that working memory loading may increase ultimatum game acceptances in between-subject experiments, a within-subject investigation found little support for this method of promoting/disrupting rational ultimatum game decisions. There were also no relationships found between switching or inhibition executive functions and ultimatum game responder decisions. A moderate positive relationship was found between updating executive function and ultimatum game acceptance rates but this relationship was dependent on working memory task feedback, a within-subject design and active loading of the working memory system. However, its possible that these findings only apply to within-subject paradigms and future between-subject studies are advised.

Securing access to affordable energy services is of central importance to our societies. To do this sustainably, energy systems design should be – amongst other things – environmentally compliant and reconcile with the integrated management of potentially limiting resources. This work considers the role for so-called 'Smart Grids' to improve the delivery of energy services. It deals with the integration of renewable energy technologies to mitigate climate change. It further demonstrates an approach to harmonise potentially conflicting energy, water and land-use strategies. Each presents particular challenges to energy systems analysis. Computer aided models can help identify energy systems that most effectively meet the multiple demands placed on them. As models constitute a simple abstraction of reality, it is important to ensure that those dynamics that considerably impact results are suitably integrated. In its three parts, this thesis extends long-term energy system models to consider improved integration between: (A) supply and demand through Smart Grids; (B) timeframes by incorporating short-term operating constraints into long-term models; and (C) resource systems by linking multiple modelling tools. In Part A, the thesis explores the potential of Smart Grids to accelerate and improve electrification efforts in developing countries. Further, a long-term energy system model is enhanced to investigate the Smart Grid benefits associated with a closer integration of supply, storage and demand-side options. In Part B, the same model is extended to integrate flexibility requirements. The benefits of this integration are illustrated on an Irish case study on high levels of wind power penetrations. In Part C, an energy model is calibrated to consider climate change scenarios and linkages with land-use and water models. This serves to assess the implications of introducing biofuels on the small island developing state of Mauritius. The thesis demonstrates that too weak integration between models and resource systems can produce significantly diverging results. The system configurations derived may consequently generate different – and potentially erroneous – policy and investment insights. / Säker och prisvärd tillgång till energitjänster är en central fråga för dagens samhällen. För att tillgodose samhällen med hållbara energitjänster bör energisystemen designas för att – bland annat – möta de miljömässiga kraven samt hantera potentiellt begränsade resurser. Den här avhandlingen undersöker de ”smarta” elnätens roll för bättre tillhandahållande av energitjänster. Avhandlingen behandlar integration av förnybar energiteknik för minskad klimatpåverkan samt demonstrerar ett tillvägagångssätt för att förena potentiellt motstridiga energi-, vatten- och markanvändningsstrategier. Dessa uppvisar särskilda utmaningar i energisystemanalyser. Datorstödda modeller kan användas för att identifiera energisystem som på effektivast sätt möter samhällets krav. Datorstödda modeller är, per definition, förenklingar av verkligheten och det är därför viktigt att säkerställa en korrekt representation av det verkliga systemets dynamik. Den här avhandlingen förstärker energisystemmodeller för långsiktsprognoser utifrån tre aspekter: förbättra integrationen av (A) tillgång och efterfrågan genom smarta elnät; (B) olika tidsaspekter genom att inkludera kortsiktiga operativa begränsningar; samt (C) resurssystem genom att sammanlänka olika modelleringsverktyg. I del A utforskades de smarta elnätens potential för att förbättra elektriska system i utvecklingsländer. En befintlig energisystemmodell förstärktes för att behandla smarta elnät och kan därmed fånga fördelarna förknippade med energilagring och energianvändning. I del B utvidgades en energisystemmodell för långsiktsprognoser med flexibilitet för kortsiktiga operativa begränsningar. En fallstudie fokuserad på ett vindkraftsdominerat irländskt elnät genomfördes för att demonstrera fördelarna av modellutvecklingen. I del C kalibrerades en energisystemmodell för att ta klimatscenarier i beaktande samt energisystemets kopplingar till markanvändning och vattenresurssystem. En fallstudie fokuserad på Mauritius energisystem genomfördes för att undersöka konsekvenserna av en potentiell introducering av biobränslen. Avhandlingen demonstrerar att undermålig integration av energimodeller och resurssystem kan leda till avsevärda avvikelser i resultaten. Slutsatser som dras utifrån dessa resultat kan därmed leda till vitt skilda – och potentiellt felaktiga – underlag för investeringar och energipolitiska rekommendationer. / <p>QC 20131118</p>

Thesis advisor: Hilary Palevsky / The ocean plays a key role in global carbon cycling, taking up CO2 from the atmosphere. A fraction of this CO2 is converted into organic carbon through primary production in the surface ocean and sequestered in the deep ocean through a process known as the biological pump. The ability of the biological pump to sequester carbon away from the atmosphere is influenced by the interaction between the annual cycle of ocean mixed layer depth (MLD), primary production, and ecosystem processes that influence export efficiency. Gravitational sinking of particulate organic carbon (POC) is the largest component of the biological pump and the aspect that is best represented in Earth System Models (ESMs). I use ESM data from CESM2, an ESM participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), to investigate how a high-emissions climate change scenario will impact POC flux globally and regionally over the 21st century. The model simulates a 4.4% decrease in global POC flux at the 100 m depth horizon, from 7.12 Pg C/yr in the short-term (2014-2034) to 6.81 Pg C/yr in the long-term (2079-2099), indicating that the biological pump will become less efficient overall at sequestering carbon. However, the extent of change varies across the globe, including the largest POC flux declines in the North Atlantic, where the maximum annual MLD is projected to shoal immensely. In the future, a multi-model comparison across ESMs will allow for further analysis on the variability of these changes to the biological pump. / Thesis (BS) — Boston College, 2021. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Earth and Environmental Science.

Solar PV-based distributed generation has increased significantly over the last few years, and the rapid growth is expected to continue in the foreseeable future. As the penetration levels of distributed generation increase, power systems will become increasingly decentralized with bi-directional flow of electricity between the transmission and distribution networks. To manage such decentralized power systems, planners and operators need models that accurately reflect the structure of, and interactions between the transmission and distribution networks. Moreover, algorithms that can simulate the steady state and dynamics of power systems using these models are also needed. In this context, integrated transmission and distribution system modeling and simulation has become an important research area in recent years, and the primary focus so far has been on studying the steady state response of power systems using integrated transmission and distribution system models. The primary objective of this dissertation is to develop an analysis approach and a program that can simulate the dynamics of three phase, integrated transmission and distribution system models, and use the program to demonstrate the advantages of evaluating the impact of solar PV-based distributed generation on power systems dynamics using such models. To realize this objective, a new dynamic simulation analysis approach is presented, the implementation of the approach in a program is discussed, and verification studies are presented to demonstrate the accuracy of the program. A new dynamic model for small solar PV-based distributed generation is also investigated. This model can interface with unbalanced networks and change its real power output according to the incident solar irradiation. Finally, application of the dynamic simulation program for evaluating the impact of solar PV units using an integrated transmission and distribution system model is discussed. The dissertation presents a new approach for studying the impact of solar PV-based distributed generation on power systems dynamics, and demonstrates that the solar PV impact studies performed using the program and integrated transmission and distribution system models provide insights about the dynamic response of power systems that cannot be obtained using traditional dynamic simulation approaches that rely on transmission only models. / Ph. D.

abstract: Earth-system models describe the interacting components of the climate system and technological systems that affect society, such as communication infrastructures. Data assimilation addresses the challenge of state specification by incorporating system observations into the model estimates. In this research, a particular data assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is applied to the ionosphere, which is a domain of practical interest due to its effects on infrastructures that depend on satellite communication and remote sensing. This dissertation consists of three main studies that propose strategies to improve space- weather specification during ionospheric extreme events, but are generally applicable to Earth-system models: Topic I applies the LETKF to estimate ion density with an idealized model of the ionosphere, given noisy synthetic observations of varying sparsity. Results show that the LETKF yields accurate estimates of the ion density field and unobserved components of neutral winds even when the observation density is spatially sparse (2% of grid points) and there is large levels (40%) of Gaussian observation noise. Topic II proposes a targeted observing strategy for data assimilation, which uses the influence matrix diagnostic to target errors in chosen state variables. This strategy is applied in observing system experiments, in which synthetic electron density observations are assimilated with the LETKF into the Thermosphere-Ionosphere- Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm. Results show that assimilating targeted electron density observations yields on average about 60%–80% reduction in electron density error within a 600 km radius of the observed location, compared to 15% reduction obtained with randomly placed vertical profiles. Topic III proposes a methodology to account for systematic model bias arising ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap- plied with the TIEGCM during a geomagnetic storm, and is used to estimate the spatiotemporal variations of bias in electron density predictions during the transitionary phases of the geomagnetic storm. Results show that this strategy reduces error in 1-hour predictions of electron density by about 35% and 30% in polar regions during the main and relaxation phases of the geomagnetic storm, respectively. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2018

No description available.

This thesis considers the development of a Hammerstein-bilinear approach to non-linear systems modelling, analysis and control systems design, which builds on and extends the applicability of an existing bilinear approach. The underlying idea of the Hammerstein-bilinear approach is to use the Hammerstein-bilinear system models to capture various physical phenomena of interest and subsequently use these for model based control system designs with the premise being that of achieving enhanced control performance. The advantage of the Hammerstein-bilinear approach is that the well-structured system models allow techniques that have been originally developed for linear systems to be extended and applied, while retaining moderate complexity of the corresponding system identification schemes and nonlinear model based control designs. In recognition of the need to be able to identify the Hammerstein-bilinear models a unified suite of algorithms, being the extensions to the simplified refined instrumental variable method for parameter estimation of linear transfer function models is proposed. These algorithms are able to operate in both the continuous-time and discrete-time domains to reflect the requirements of the intended purposes of the identified models with the emphasis being placed on straightforward applicability of the developed algorithms and recognising the need to be able to operate under realistic practical system identification scenarios. Moreover, the proposed algorithms are also applicable to parameter estimation of Hammerstein and bilinear models, which are special cases of the wider Hammerstein-bilinear model class. The Hammerstein-bilinear approach has been applied to an industrial heating, ventilation and air conditioning (HVAC) system, which has also been the underlying application addressed in this thesis. A unique set of dynamic control design purpose oriented air temperature and humidity Hammerstein-bilinear models of an environmentally controlled clear room manufacturing zone has been identified. The greater insights afforded by the knowledge of the system nonlinearities then allow for enhanced control tuning of the associated commercial HVAC control system leading to an improved overall control performance.